Special issue
2022, 37(1):1-20.
DOI: 10.16337/j.1004-9037.2022.01.001
Abstract:
In modern electronic warfare， the competition between electronic interference and anti-interference is becoming more and more fierce， which has become a hotspot in the radar countermeasure field to develop the identification algorithms for radar active jamming. This paper analyzes the radar active jamming recognition algorithm in details， and summarizes the general process of jamming identification methods in the world. Firstly， the types of common radar jamming are divided， and the jamming mechanism and the signal model of current common radar active jamming signal are introduced in details. Then from the feature-extraction means and the design of the classifiers， the flow of the jamming identification algorithm are analyzed comprehensively. Finally， the future development directions of the radar active jamming identification algorithms are prospected.
In modern electronic warfare， the competition between electronic interference and anti-interference is becoming more and more fierce， which has become a hotspot in the radar countermeasure field to develop the identification algorithms for radar active jamming. This paper analyzes the radar active jamming recognition algorithm in details， and summarizes the general process of jamming identification methods in the world. Firstly， the types of common radar jamming are divided， and the jamming mechanism and the signal model of current common radar active jamming signal are introduced in details. Then from the feature-extraction means and the design of the classifiers， the flow of the jamming identification algorithm are analyzed comprehensively. Finally， the future development directions of the radar active jamming identification algorithms are prospected.
2022, 37(5):952-970.
DOI: 10.16337/j.1004-9037.2022.05.002
Abstract:
With the development of unmanned aerial vehicle （UAV） software and hardware technology， the flying Ad-Hoc networks （FANETs） formed by the self-organization of multiple UAV clusters have received more and more attention from academia and industry. Its flexible deployment and rapid response capabilities enable it to complete a variety of tasks efficiently and inexpensively. Moreover， the UAV routing protocol is one of the most critical methods to improve the quality of service （QoS）. The mobility and dynamics of FANETs make the design of routing protocols face severe challenges. Conventional mobile routing protocols cannot sufficiently meet the routing requirements of FANETs. Therefore， researchers have proposed a variety of UAV self-organizing network routing protocols from the perspectives of topology， geography， and layering， aiming to overcome mobility and improve network QoS. This paper facuses on UAV Ad-Hoc networks， categorizes and summarizes routing protocols from different routing decision-making methods， and prospects future research directions.
With the development of unmanned aerial vehicle （UAV） software and hardware technology， the flying Ad-Hoc networks （FANETs） formed by the self-organization of multiple UAV clusters have received more and more attention from academia and industry. Its flexible deployment and rapid response capabilities enable it to complete a variety of tasks efficiently and inexpensively. Moreover， the UAV routing protocol is one of the most critical methods to improve the quality of service （QoS）. The mobility and dynamics of FANETs make the design of routing protocols face severe challenges. Conventional mobile routing protocols cannot sufficiently meet the routing requirements of FANETs. Therefore， researchers have proposed a variety of UAV self-organizing network routing protocols from the perspectives of topology， geography， and layering， aiming to overcome mobility and improve network QoS. This paper facuses on UAV Ad-Hoc networks， categorizes and summarizes routing protocols from different routing decision-making methods， and prospects future research directions.
2022, 37(6):1179-1197.
DOI: 10.16337/j.1004-9037.2022.06.001
Abstract:
For the problem that spectrum resources is increasingly scare in electromagnetic spectrum space， the radio frequency machine learning （RFML） is purposed to design special machine learning models by introducing domain knowledge. It has the advantages of fast， few sample or even zero sample， interpretability and high performance. The state-of-the-art RFML in wireless communication is analyzed from the five layers， which are physical layer， data link layer， network layer， transmission layer and application layer. Moreover， based on the existing achievements， four RFML frameworks （serial/parallel/coupled/feedback dual-driven framework） are summarized by the interaction relationship of the data-driven model and the knowledge-driven model. Finally， the key challenges and open issues are identified and elaborated to facilitate the RFML research and practical applications.
For the problem that spectrum resources is increasingly scare in electromagnetic spectrum space， the radio frequency machine learning （RFML） is purposed to design special machine learning models by introducing domain knowledge. It has the advantages of fast， few sample or even zero sample， interpretability and high performance. The state-of-the-art RFML in wireless communication is analyzed from the five layers， which are physical layer， data link layer， network layer， transmission layer and application layer. Moreover， based on the existing achievements， four RFML frameworks （serial/parallel/coupled/feedback dual-driven framework） are summarized by the interaction relationship of the data-driven model and the knowledge-driven model. Finally， the key challenges and open issues are identified and elaborated to facilitate the RFML research and practical applications.
2022, 37(6):1198-1207.
DOI: 10.16337/j.1004-9037.2022.06.002
Abstract:
With the increasing scarcity of spectrum resources， the increasing severity of radio regulations， and the increasing fierceness of electromagnetic warfare， the research of the electromagnetic spectrum situational has to transit from spectrum perception to spectrum cognition. This paper reviews the current research status of the theories and methods for spectrum situational cognition from four aspects： The mathematical models and fundamental mechanisms for a multi-layer spectrum situational cognition system， the extraction and fusion of cross-region multi-dimensional electromagnetic features， the efficient completion and prediction for spectrum situation， and the precise inference and intention judgment for spectrum-related behaviors. To address the complex multi-domain multi-dimensional electromagnetic spectrum environment and diversified tasks， it is critical to develop a framework of spectrum situational cognition from “data to semantics” “semantics to behaviors” “a single region to cross regions” “a single layer to multi layers”. This framework builds up foundations， both theoretically and technically， for efficient spectrum sharing in space-terrestrial integrated information networks， efficient radio regulations， and advantages in the electromagnetic spectrum warfare.
With the increasing scarcity of spectrum resources， the increasing severity of radio regulations， and the increasing fierceness of electromagnetic warfare， the research of the electromagnetic spectrum situational has to transit from spectrum perception to spectrum cognition. This paper reviews the current research status of the theories and methods for spectrum situational cognition from four aspects： The mathematical models and fundamental mechanisms for a multi-layer spectrum situational cognition system， the extraction and fusion of cross-region multi-dimensional electromagnetic features， the efficient completion and prediction for spectrum situation， and the precise inference and intention judgment for spectrum-related behaviors. To address the complex multi-domain multi-dimensional electromagnetic spectrum environment and diversified tasks， it is critical to develop a framework of spectrum situational cognition from “data to semantics” “semantics to behaviors” “a single region to cross regions” “a single layer to multi layers”. This framework builds up foundations， both theoretically and technically， for efficient spectrum sharing in space-terrestrial integrated information networks， efficient radio regulations， and advantages in the electromagnetic spectrum warfare.
2022, 37(6):1208-1217.
DOI: 10.16337/j.1004-9037.2022.06.003
Abstract:
To improve the localization accuracy of the subspace data fusion （SDF） applied in distributed multi-array under the influence of unknown mutual coupling， we propose a reduced mutual coupling dimension subspace data fusion （RMCD-SDF） approach in this paper. Firstly， we introduce the mutual coupling error model into the SDF approach to make it adapt to the scenario where the antenna array is affected by the unknown mutual coupling error. Furthermore， in order to reduce the ultra-high computational complexity caused by searching all unknown parameters simultaneously， we introduce the reduced-dimension idea and construct the spectral function of RMCD-SDF. Simulation results show that the RMCD-SDF approach has advantageous localization performance when arrays are affected by unknown mutual coupling. The RMCD-SDF approach has similar computational complexity but higher localization accuracy than existing algorithms. When the signal-to-noise ratio （SNR） is 10 dB， the root means square error of the proposed approach is 8.67 dB lower than the classical SDF algorithm.
To improve the localization accuracy of the subspace data fusion （SDF） applied in distributed multi-array under the influence of unknown mutual coupling， we propose a reduced mutual coupling dimension subspace data fusion （RMCD-SDF） approach in this paper. Firstly， we introduce the mutual coupling error model into the SDF approach to make it adapt to the scenario where the antenna array is affected by the unknown mutual coupling error. Furthermore， in order to reduce the ultra-high computational complexity caused by searching all unknown parameters simultaneously， we introduce the reduced-dimension idea and construct the spectral function of RMCD-SDF. Simulation results show that the RMCD-SDF approach has advantageous localization performance when arrays are affected by unknown mutual coupling. The RMCD-SDF approach has similar computational complexity but higher localization accuracy than existing algorithms. When the signal-to-noise ratio （SNR） is 10 dB， the root means square error of the proposed approach is 8.67 dB lower than the classical SDF algorithm.
2022, 37(1):228-239.
DOI: 10.16337/j.1004-9037.2022.01.020
Abstract:
The cooperation mechanism in unmanned aerial vehicle（UAV） ad-hoc network is studied. A cooperative time division multiple access （TDMA） protocol for UAV ad-hoc network based on dynamic relay selection is proposed. The protocol introduces the dual-queue cooperation mechanism when transmitting the relay packets， an independent media access control （MAC） layer relay packet buffer queue is introduced in addition to the network layer packet buffer queue. The protocol can also realize the dynamic selection of the default relay node and the helper relay node， so as to adapt to the heavy traffic load and the rapid change of network topology. The simulation results show that the through the dynamic selection mechanism of the relay node， the proposed cooperative TDMA protocol can obtain higher packet delivery rate and lower end-to-end delay than the traditional TDMA protocol and opportunistic cooperative relay time division multiple access （OCR-TDMA） protocol， when the network traffic load is heavy and the topology changes rapidly.
The cooperation mechanism in unmanned aerial vehicle（UAV） ad-hoc network is studied. A cooperative time division multiple access （TDMA） protocol for UAV ad-hoc network based on dynamic relay selection is proposed. The protocol introduces the dual-queue cooperation mechanism when transmitting the relay packets， an independent media access control （MAC） layer relay packet buffer queue is introduced in addition to the network layer packet buffer queue. The protocol can also realize the dynamic selection of the default relay node and the helper relay node， so as to adapt to the heavy traffic load and the rapid change of network topology. The simulation results show that the through the dynamic selection mechanism of the relay node， the proposed cooperative TDMA protocol can obtain higher packet delivery rate and lower end-to-end delay than the traditional TDMA protocol and opportunistic cooperative relay time division multiple access （OCR-TDMA） protocol， when the network traffic load is heavy and the topology changes rapidly.
2022, 37(3):680-694.
DOI: 10.16337/j.1004-9037.2022.03.017
Abstract:
Communication spectral behavior analysis is critical to the improvement of communication situation awareness and electromagnetic reconnaissance capability in an electromagnetic countermeasure environment. With the development of artificial intelligence technology， communication spectral behavior analysis techniques have been gradually transferred from traditional methods based on feature extraction to intelligent methods based on deep learning technology. However， the insufficient and incomplete spectrum monitoring data in the electromagnetic countermeasure environment will hinder the deep network from feature learning. Moreover， the dynamic battlefield makes it even more challenging for real-time analysis. This paper categorizes the communication spectral behavior analysis technologies into three groups： Frequency behavior analysis， network topology recognition， and communication intention inference from researching objectives in the electromagnetic countermeasure environment. Furthermore， the inner relationship between the three categories is illustrated. Finally， the existing research and development venation are reviewed and prospected considering challenges.
Communication spectral behavior analysis is critical to the improvement of communication situation awareness and electromagnetic reconnaissance capability in an electromagnetic countermeasure environment. With the development of artificial intelligence technology， communication spectral behavior analysis techniques have been gradually transferred from traditional methods based on feature extraction to intelligent methods based on deep learning technology. However， the insufficient and incomplete spectrum monitoring data in the electromagnetic countermeasure environment will hinder the deep network from feature learning. Moreover， the dynamic battlefield makes it even more challenging for real-time analysis. This paper categorizes the communication spectral behavior analysis technologies into three groups： Frequency behavior analysis， network topology recognition， and communication intention inference from researching objectives in the electromagnetic countermeasure environment. Furthermore， the inner relationship between the three categories is illustrated. Finally， the existing research and development venation are reviewed and prospected considering challenges.
2022, 37(3):695-702.
DOI: 10.16337/j.1004-9037.2022.03.018
Abstract:
ZUC-256 is a stream cipher developed in China for 5G communication and post-quantum， which mainly includes the ZUC-256 stream cipher and the integrity algorithm （EIA3）. This paper designs two kinds of hardware structures of ZUC-256 stream cipher and an EIA3 algorithm structure based on ZUC-256. And then the designed structures are implemented based on PFGA， and their performance is compared. Comparison results show that： The two new ZUC-256 designs reach a throughput of 6.72 Gb/s， which is 45.24% faster than the current ZUC-256 design， and they uses fewer resources than the previous ZUC-128 design， reducing the area by 38.48% and 30.90%， respectively. And the EIA3 algorithm based on ZUC-256 can complete encryption of 128 bit data within 0.71 μs.
ZUC-256 is a stream cipher developed in China for 5G communication and post-quantum， which mainly includes the ZUC-256 stream cipher and the integrity algorithm （EIA3）. This paper designs two kinds of hardware structures of ZUC-256 stream cipher and an EIA3 algorithm structure based on ZUC-256. And then the designed structures are implemented based on PFGA， and their performance is compared. Comparison results show that： The two new ZUC-256 designs reach a throughput of 6.72 Gb/s， which is 45.24% faster than the current ZUC-256 design， and they uses fewer resources than the previous ZUC-128 design， reducing the area by 38.48% and 30.90%， respectively. And the EIA3 algorithm based on ZUC-256 can complete encryption of 128 bit data within 0.71 μs.
2022, 37(3):703-714.
DOI: 10.16337/j.1004-9037.2022.03.019
Abstract:
In the indoor location based on Wi Fi location fingerprint， the received signal strength indicators （RSSI） collected by heterogeneous devices at the same location and time are different， which makes the offline fingerprint database incompatible with the online signals collected by different users， thus affecting the location accuracy. To solve this problem， this paper proposes a localization algorithm suitable to heterogeneous devices. In this method， the offline fingerprint database with stable signals is constructed through the selection of access point （AP）， and then the fingerprint database is standardized by procrustes analysis （PA） to eliminate the signal difference introduced by heterogeneous devices. In the online stage， the cosine similarity （CS） algorithm is used to obtain the position estimation of the target. The positioning performance of the proposed method is tested with four mobile phones in two typical indoor environments， and the factors affecting the positioning performance are analyzed. The experimental results show that the average positioning errors of the proposed method in the two indoor environments are 2.96 m and 2.29 m， which is 21.3% and 21.6% higher than those of the Weight K-nearest neighbor （WKNN） algorithm， respectively.
In the indoor location based on Wi Fi location fingerprint， the received signal strength indicators （RSSI） collected by heterogeneous devices at the same location and time are different， which makes the offline fingerprint database incompatible with the online signals collected by different users， thus affecting the location accuracy. To solve this problem， this paper proposes a localization algorithm suitable to heterogeneous devices. In this method， the offline fingerprint database with stable signals is constructed through the selection of access point （AP）， and then the fingerprint database is standardized by procrustes analysis （PA） to eliminate the signal difference introduced by heterogeneous devices. In the online stage， the cosine similarity （CS） algorithm is used to obtain the position estimation of the target. The positioning performance of the proposed method is tested with four mobile phones in two typical indoor environments， and the factors affecting the positioning performance are analyzed. The experimental results show that the average positioning errors of the proposed method in the two indoor environments are 2.96 m and 2.29 m， which is 21.3% and 21.6% higher than those of the Weight K-nearest neighbor （WKNN） algorithm， respectively.
2022, 37(3):657-667.
DOI: 10.16337/j.1004-9037.2022.03.015
Abstract:
In order to solve the problems of insufficient objective evaluation and lack of evaluation basis for the classification and identification of radar emitter signal， an improved gray correlation feature evaluation model combined with interval-valued intuitionistic fuzzy thought is constructed. The model introduces the dimension of signal-to-noise ratio （SNR） to examine the dynamic differences of data at different levels， describes feature information with interval data， and establishes an interval-valued intuitionistic fuzzy comprehensive decision matrix. Secondly， an optimization model that maximizes the total deviation between features is used to determine the weight of each indicator. Finally， based on the improved gray correlation framework， the ranking of feature schemes is achieved by combining with the approach to ideal points. The simulation results show that the proposed method can give the sorting identification feature evaluation and sorting results that are consistent with the actual situation， and is basically consistent with the analysis results by the unimproved gray correlation method， which verifies the feasibility and effectiveness of the proposed method.
In order to solve the problems of insufficient objective evaluation and lack of evaluation basis for the classification and identification of radar emitter signal， an improved gray correlation feature evaluation model combined with interval-valued intuitionistic fuzzy thought is constructed. The model introduces the dimension of signal-to-noise ratio （SNR） to examine the dynamic differences of data at different levels， describes feature information with interval data， and establishes an interval-valued intuitionistic fuzzy comprehensive decision matrix. Secondly， an optimization model that maximizes the total deviation between features is used to determine the weight of each indicator. Finally， based on the improved gray correlation framework， the ranking of feature schemes is achieved by combining with the approach to ideal points. The simulation results show that the proposed method can give the sorting identification feature evaluation and sorting results that are consistent with the actual situation， and is basically consistent with the analysis results by the unimproved gray correlation method， which verifies the feasibility and effectiveness of the proposed method.
2022, 37(4):917-925.
DOI: 10.16337/j.1004-9037.2022.04.019
Abstract:
An IP core of low hardware-cost 256-point fast Fourier transform （FFT） processor is designed based on field programmable gate array （FPGA）. In order to reduce the complexity of twiddle factor calculation， the radix-24 algorithm based on decimation in frequency and the single-path delay feedback （SDF） pipelined architecture are adopted. For reducing hardware-cost， a cascade canonical signed digit （CSD） complex multiplier instead of conventional Booth multiplier is proposed for the operation of twiddle factorW 256 i ![]()
![]()
An IP core of low hardware-cost 256-point fast Fourier transform （FFT） processor is designed based on field programmable gate array （FPGA）. In order to reduce the complexity of twiddle factor calculation， the radix-24 algorithm based on decimation in frequency and the single-path delay feedback （SDF） pipelined architecture are adopted. For reducing hardware-cost， a cascade canonical signed digit （CSD） complex multiplier instead of conventional Booth multiplier is proposed for the operation of twiddle factor
2022, 37(6):1259-1267.
DOI: 10.16337/j.1004-9037.2022.06.007
Abstract:
In order to improve the estimation accuracy and convergence speed of millimeter-wave multiple-input multiple-output （MIMO） cascaded channel assisted by intelligent reflective surface （IRS）， the conventional bilinear alternating least squares （BALS） algorithm is improved to ω-BALS algorithm with relaxation factor and regularized T-BALS， to speeds up the convergence speed and stability based on parallel factor （PARAFAC） decomposition. When one of the numbers of array antennas on the base station， IRS element or user side is large， an improved （Singular value decomposition，svd）-BALS algorithm is proposed. The algorithm reconstructs the mode-n matrices by compressing it into a low-dimensional core tensor via singular value decomposition. Simulation results show that the normalized mean squared error performance of the algorithm is improved and the convergence speed is accelerated.
In order to improve the estimation accuracy and convergence speed of millimeter-wave multiple-input multiple-output （MIMO） cascaded channel assisted by intelligent reflective surface （IRS）， the conventional bilinear alternating least squares （BALS） algorithm is improved to ω-BALS algorithm with relaxation factor and regularized T-BALS， to speeds up the convergence speed and stability based on parallel factor （PARAFAC） decomposition. When one of the numbers of array antennas on the base station， IRS element or user side is large， an improved （Singular value decomposition，svd）-BALS algorithm is proposed. The algorithm reconstructs the mode-n matrices by compressing it into a low-dimensional core tensor via singular value decomposition. Simulation results show that the normalized mean squared error performance of the algorithm is improved and the convergence speed is accelerated.
2022, 37(6):1268-1279.
DOI: 10.16337/j.1004-9037.2022.06.008
Abstract:
In wireless channel modeling and simulation， it is of great significance to realize a high-efficiency and high-accuracy wireless channel prediction method. Aiming at this request， a wireless channel prediction method based on multi-population genetic algorithm-back propagation （MPGA-BP） neural network is proposed. This method optimizes the structure parameters of BP neural network by improving the genetic algorithm， thereby improving the problem of poor prediction accuracy of the BP neural network and greatly improving the prediction performance of the BP neural network. In this paper， the theoretical value of ray tracing algorithm is combined with BP neural network to realize a more efficient wireless channel prediction method. By comparing the prediction errors of the genetic algorithm （GA）-BP neural network model and the MPGA-BP neural network model， it is found that the prediction results of the MPGA-BP neural network model are better than the GA-BP neural network model， which proves that the proposed wireless channel prediction method has good accuracy. Therefore， the wireless channel prediction can be performed more efficiently.
In wireless channel modeling and simulation， it is of great significance to realize a high-efficiency and high-accuracy wireless channel prediction method. Aiming at this request， a wireless channel prediction method based on multi-population genetic algorithm-back propagation （MPGA-BP） neural network is proposed. This method optimizes the structure parameters of BP neural network by improving the genetic algorithm， thereby improving the problem of poor prediction accuracy of the BP neural network and greatly improving the prediction performance of the BP neural network. In this paper， the theoretical value of ray tracing algorithm is combined with BP neural network to realize a more efficient wireless channel prediction method. By comparing the prediction errors of the genetic algorithm （GA）-BP neural network model and the MPGA-BP neural network model， it is found that the prediction results of the MPGA-BP neural network model are better than the GA-BP neural network model， which proves that the proposed wireless channel prediction method has good accuracy. Therefore， the wireless channel prediction can be performed more efficiently.
2022, 37(6):1280-1287.
DOI: 10. 16337/j. 1004-9037. 2022. 06. 009
Abstract:
Since a single feature is not enough to comprehensively represent the subtle feature differences and thus limit the recognition rate for specific identification of communication emitter， a method of specific identification of communication emitter based on feature fusion is proposed. Firstly， the short-time Fourier transform and bispectrum transform are applied to the original signal to extract time-frequency features and bispectrum features. Secondly， the wavelet fusion technology is integrated to carry out feature fusion. Finally， the residual neural network is used to mine the hidden deep features of the signal to complete classification and recognition. Experimental results show that compared with the single feature method， the recognition effect of the short wave communication signal transmitted by analog signal source after feature fusion has higher recognition accuracy， and it has better recognition effect under the condition of low signal-to-noise ratio（SNR）.
Since a single feature is not enough to comprehensively represent the subtle feature differences and thus limit the recognition rate for specific identification of communication emitter， a method of specific identification of communication emitter based on feature fusion is proposed. Firstly， the short-time Fourier transform and bispectrum transform are applied to the original signal to extract time-frequency features and bispectrum features. Secondly， the wavelet fusion technology is integrated to carry out feature fusion. Finally， the residual neural network is used to mine the hidden deep features of the signal to complete classification and recognition. Experimental results show that compared with the single feature method， the recognition effect of the short wave communication signal transmitted by analog signal source after feature fusion has higher recognition accuracy， and it has better recognition effect under the condition of low signal-to-noise ratio（SNR）.
2022, 37(6):1288-1296.
DOI: 10.16337/j.1004-9037.2022.06.010
Abstract:
The optimization of resource allocation in wireless communication networks can be described as a mixed integer nonlinear programming （MINLP） problem. It is essentially a non-convex NP hard problem. In order to reduce the computational complexity and ensure the optimal performance of the allocation scheme， a binary whale optimization algorithm （WOA） is proposed to allocate wireless resources. Based on the original WOA position update is carried out based on the switch between values 1 and 0. The current position changes are determined by the probability calculated by the humpback spiral movement. Meanwhile， different transfer functions are used to map the continuous search space to discrete actions， and the penalty method and the optimization constraint processing are introduced. Two cases of resource allocation in wireless networks are analyzed in the experiment： The power allocation problem with maximum confidentiality and the mobile edge computing migration. The results show that the proposed method has excellent system performance and obtains similar effects to other methods， but its convergence speed is faster and its complexity is lower.
The optimization of resource allocation in wireless communication networks can be described as a mixed integer nonlinear programming （MINLP） problem. It is essentially a non-convex NP hard problem. In order to reduce the computational complexity and ensure the optimal performance of the allocation scheme， a binary whale optimization algorithm （WOA） is proposed to allocate wireless resources. Based on the original WOA position update is carried out based on the switch between values 1 and 0. The current position changes are determined by the probability calculated by the humpback spiral movement. Meanwhile， different transfer functions are used to map the continuous search space to discrete actions， and the penalty method and the optimization constraint processing are introduced. Two cases of resource allocation in wireless networks are analyzed in the experiment： The power allocation problem with maximum confidentiality and the mobile edge computing migration. The results show that the proposed method has excellent system performance and obtains similar effects to other methods， but its convergence speed is faster and its complexity is lower.
2022, 37(6):1297-1313.
DOI: 10.16337/j.1004-9037.2022.06.011
Abstract:
Unmanned aerial vehicles （UAVs）assisted simultaneous wireless information and power transfer （SWIPT） can be used to improve energy efficiency of Internet of Things （IoT）. It can replenish energy for ground devices in IoT to complete the task of information-receiving uninterruptedly. In the face of the limited energy of UAVs and the demand for improving energy efficiency， this paper studies an energy-efficient UAV-ground communication optimization problem. We jointly optimize UAV transmit power and power splitting ratio， and design UAV trajectory and ground devices wake-up time allocation， in which UAV propulsion energy consumption and energy demand of ground devices are considered comprehensively. The formulated energy-efficiency maximization problem is a non-convex， fractional and mixed integer programming problem. To solve this problem， this paper proposes an alternate iterative optimization algorithm based on the successive convex approximation （SCA） and the classical Dinkelbach method. Finally， simulation results verify the effectiveness and superiority of the proposed algorithm.
Unmanned aerial vehicles （UAVs）assisted simultaneous wireless information and power transfer （SWIPT） can be used to improve energy efficiency of Internet of Things （IoT）. It can replenish energy for ground devices in IoT to complete the task of information-receiving uninterruptedly. In the face of the limited energy of UAVs and the demand for improving energy efficiency， this paper studies an energy-efficient UAV-ground communication optimization problem. We jointly optimize UAV transmit power and power splitting ratio， and design UAV trajectory and ground devices wake-up time allocation， in which UAV propulsion energy consumption and energy demand of ground devices are considered comprehensively. The formulated energy-efficiency maximization problem is a non-convex， fractional and mixed integer programming problem. To solve this problem， this paper proposes an alternate iterative optimization algorithm based on the successive convex approximation （SCA） and the classical Dinkelbach method. Finally， simulation results verify the effectiveness and superiority of the proposed algorithm.
2022, 37(6):1314-1322.
DOI: 10.16337/j.1004-9037.2022.06.012
Abstract:
Targeting the low efficiency and privacy leakage of intra-city delivery of confidential documents， a intra-city path planning algorithm based on block-chain is proposed. It adaptively generates the shortest path to protect location privacy in real time. With the consensus mechanism and smart contract algorithm of block-chain， the distributed site is selected by route planning with homomorphic encryption. The vehicle can encrypt and decrypt the next site information by using its own context attribute， and be equipped with anti-impersonation. This algorithm also solves the problem of mutual distrust among vehicles， sites and deliveries. Finally， through experiments， the impact of the homomorphic calculation results of smart contracts， the number of different contextual attributes， and the number of different sites on the calculation cost of path planning is analyzed. The results show that the algorithm of the intracity delivery system has the capabilities of confidentiality， integrity and anti-tampering and can ensure high-delivery efficiency.
Targeting the low efficiency and privacy leakage of intra-city delivery of confidential documents， a intra-city path planning algorithm based on block-chain is proposed. It adaptively generates the shortest path to protect location privacy in real time. With the consensus mechanism and smart contract algorithm of block-chain， the distributed site is selected by route planning with homomorphic encryption. The vehicle can encrypt and decrypt the next site information by using its own context attribute， and be equipped with anti-impersonation. This algorithm also solves the problem of mutual distrust among vehicles， sites and deliveries. Finally， through experiments， the impact of the homomorphic calculation results of smart contracts， the number of different contextual attributes， and the number of different sites on the calculation cost of path planning is analyzed. The results show that the algorithm of the intracity delivery system has the capabilities of confidentiality， integrity and anti-tampering and can ensure high-delivery efficiency.
2022, 37(6):1323-1332.
DOI: 10.16337/j.1004-9037.2022.06.013
Abstract:
In the cyber-physical system （CPS）， the traditional multi-source heterogeneous data integration model is difficult to map the conceptual layer between heterogeneous systems through middleware， which has the problems of low transmission performance and difficult system expansion. Due to the challenges above， a CPS-oriented heterogeneous data integration model is proposed. The data object model is designed to realize the high-level concept mapping between physical and simulation systems. The monitoring and control metadata are defined and the incremental or full field updates for different data types are used to reduce network load. A communication model of system is designed based on the Protobuf protocol to improve the system expansion capability. Based on the data interaction model and high level architecture （HLA）/data distribution service （DDS） system middleware， a CPS prototype system is implemented， which verifies the usability of the model and compares the compression performance of the message.
In the cyber-physical system （CPS）， the traditional multi-source heterogeneous data integration model is difficult to map the conceptual layer between heterogeneous systems through middleware， which has the problems of low transmission performance and difficult system expansion. Due to the challenges above， a CPS-oriented heterogeneous data integration model is proposed. The data object model is designed to realize the high-level concept mapping between physical and simulation systems. The monitoring and control metadata are defined and the incremental or full field updates for different data types are used to reduce network load. A communication model of system is designed based on the Protobuf protocol to improve the system expansion capability. Based on the data interaction model and high level architecture （HLA）/data distribution service （DDS） system middleware， a CPS prototype system is implemented， which verifies the usability of the model and compares the compression performance of the message.
2022, 37(6):1246-1258.
DOI: 10.16337/j.1004-9037.2022.06.006
Abstract:
Waveform design for detection and jamming integration is one of the key technologies to improve radar radio frequency stealth performance. This paper proposes two integrated waveform composite coding schemes， and Wigner distribution is utilized to analyze the jamming performance of the designed waveform. Firstly， the design scheme and signal model of frequency coding， phase coding and two composite coding waveforms are given based on chaos theory. Then， the detection performance is described by ambiguity function and the jamming performance is described by information distance based on Wigner distribution. Simulation results show that the designed integrated waveform can effectively improve the detection and jamming performances， and there exists a tradeoff relationship between the above-mentioned performances.
Waveform design for detection and jamming integration is one of the key technologies to improve radar radio frequency stealth performance. This paper proposes two integrated waveform composite coding schemes， and Wigner distribution is utilized to analyze the jamming performance of the designed waveform. Firstly， the design scheme and signal model of frequency coding， phase coding and two composite coding waveforms are given based on chaos theory. Then， the detection performance is described by ambiguity function and the jamming performance is described by information distance based on Wigner distribution. Simulation results show that the designed integrated waveform can effectively improve the detection and jamming performances， and there exists a tradeoff relationship between the above-mentioned performances.
2022, 37(6):1218-1227.
DOI: 10.16337/j.1004-9037.2022.06.004
Abstract:
With the improvement of modern radar signal quality and resolution， radar systems can capture more detail on targets. Objects imaged by inverse synthetic aperture radar （ISAR） may have components that rotate at high speeds relative to the whole， such as propeller blades of aircraft. The micro-Doppler effects created by these components may seriously interfere with the imaging results. Therefore， this paper proposes a method to eliminate the micro-Doppler effect in ISAR based on the time recursive iterative adaptive （TRIAA） and compressive sensing. The method uses TRIAA technology to analyze the time-frequency characteristics of signals， removes the micro-Doppler effect from the time-frequency map， and uses the compressive sensing sparse reconstruction technology to accurately restore the effective signal. Experimental results of simulation and measured data verify the effectiveness of the proposed method.
With the improvement of modern radar signal quality and resolution， radar systems can capture more detail on targets. Objects imaged by inverse synthetic aperture radar （ISAR） may have components that rotate at high speeds relative to the whole， such as propeller blades of aircraft. The micro-Doppler effects created by these components may seriously interfere with the imaging results. Therefore， this paper proposes a method to eliminate the micro-Doppler effect in ISAR based on the time recursive iterative adaptive （TRIAA） and compressive sensing. The method uses TRIAA technology to analyze the time-frequency characteristics of signals， removes the micro-Doppler effect from the time-frequency map， and uses the compressive sensing sparse reconstruction technology to accurately restore the effective signal. Experimental results of simulation and measured data verify the effectiveness of the proposed method.
2022, 37(6):1228-1245.
DOI: 10.16337/j.1004-9037.2022.06.005
Abstract:
Due to the rapid development of 5G cellular network， its coverage will be increasingly better， thus cellular network localization is a very promising technical object for research. This paper is inspired by the fingerprint localization method in wireless localization. Under the premise that the time cost of data collection is similar， a high-speed， high-precision and low-occupancy localization method is accomplished by using the emerging deep learning technology instead of the heavy fingerprint library application and distance calculation in the localization process of fingerprint localization. In this method， a convolutional neural network is built， and the training set is constructed by selecting the appropriate input data format based on the amount of features， such as received signal intensity indication， phase and direction of arrival， of the 5G antenna signal. The trained convolutional neural network can replace the huge fingerprint library in fingerprint localization， which is very beneficial to achieve localization directly in 5G mobile devices. In addition， although convolutional neural networks consume a lot of time during the training process， the classification and localization performed after the training is completed with high speed， which can guarantee the real-time implementation of localization. The trained convolutional neural network in this paper takes up less than 0.5 MB of space for weights and biases， and is able to achieve a localization accuracy rate of 95% and an average localization accuracy of 0.1 m in the real-world environment.
Due to the rapid development of 5G cellular network， its coverage will be increasingly better， thus cellular network localization is a very promising technical object for research. This paper is inspired by the fingerprint localization method in wireless localization. Under the premise that the time cost of data collection is similar， a high-speed， high-precision and low-occupancy localization method is accomplished by using the emerging deep learning technology instead of the heavy fingerprint library application and distance calculation in the localization process of fingerprint localization. In this method， a convolutional neural network is built， and the training set is constructed by selecting the appropriate input data format based on the amount of features， such as received signal intensity indication， phase and direction of arrival， of the 5G antenna signal. The trained convolutional neural network can replace the huge fingerprint library in fingerprint localization， which is very beneficial to achieve localization directly in 5G mobile devices. In addition， although convolutional neural networks consume a lot of time during the training process， the classification and localization performed after the training is completed with high speed， which can guarantee the real-time implementation of localization. The trained convolutional neural network in this paper takes up less than 0.5 MB of space for weights and biases， and is able to achieve a localization accuracy rate of 95% and an average localization accuracy of 0.1 m in the real-world environment.
2022, 37(1):217-227.
DOI: 10.16337/j.1004-9037.2022.01.019
Abstract:
Aiming at the different threat levels under phased array radar multi-target tracking， the Bayesian Cramer-Rao lower bound （BCRLB） of the target position estimation is used as the allocation criterion. The paper establishes a multi-target tracking time resource allocation optimization model based on the threat degree. The model based on the threat degree to track the target can be divided into two categories and different types use different time resource allocation methods. Due to the time-consuming operation and optimization algorithm， this paper also proposes a multi-target tracking time resource fitting method based on back propagation（BP） neural network. Computer simulation shows that the model and the method can keep the target tracking in the best state， and the BP neural network reduces time consumption by more than two thousand times.
Aiming at the different threat levels under phased array radar multi-target tracking， the Bayesian Cramer-Rao lower bound （BCRLB） of the target position estimation is used as the allocation criterion. The paper establishes a multi-target tracking time resource allocation optimization model based on the threat degree. The model based on the threat degree to track the target can be divided into two categories and different types use different time resource allocation methods. Due to the time-consuming operation and optimization algorithm， this paper also proposes a multi-target tracking time resource fitting method based on back propagation（BP） neural network. Computer simulation shows that the model and the method can keep the target tracking in the best state， and the BP neural network reduces time consumption by more than two thousand times.
2022, 37(3):502-511.
DOI: 10.16337/j.1004-9037.2022.03.003
Abstract:
The evolution of massive multiple-input multiple-output （MIMO） techniques is an important support for further improving the performance of six-generation （6G） wireless communication systems. However， with the continuous expansion of large-scale antenna arrays， frequency division duplex （FDD） massive MIMO systems are facing severe challenges in acquiring downlink channel state information （CSI）. Deep learning has a powerful ability to learn and process high-dimensional data， which provides a potential solution to this challenge. In this paper， we survey FDD massive MIMO downlink CSI acquisition techniques based on deep learning， including CSI feedback and prediction techniques. Firstly， the theoretical frameworks of CSI feedback and prediction based on deep learning are presented. Then， the superior performance of relevant research results at home and abroad is analyzed， providing a reference scheme for solving the problem of acquiring downlink CSI in FDD massive MIMO systems towards 6G. Finally， unsolved open problems of FDD massive MIMO downlink CSI acquisition are discussed， followed by potential solutions correspondingly.
The evolution of massive multiple-input multiple-output （MIMO） techniques is an important support for further improving the performance of six-generation （6G） wireless communication systems. However， with the continuous expansion of large-scale antenna arrays， frequency division duplex （FDD） massive MIMO systems are facing severe challenges in acquiring downlink channel state information （CSI）. Deep learning has a powerful ability to learn and process high-dimensional data， which provides a potential solution to this challenge. In this paper， we survey FDD massive MIMO downlink CSI acquisition techniques based on deep learning， including CSI feedback and prediction techniques. Firstly， the theoretical frameworks of CSI feedback and prediction based on deep learning are presented. Then， the superior performance of relevant research results at home and abroad is analyzed， providing a reference scheme for solving the problem of acquiring downlink CSI in FDD massive MIMO systems towards 6G. Finally， unsolved open problems of FDD massive MIMO downlink CSI acquisition are discussed， followed by potential solutions correspondingly.
2023, 38(5):1151-1161.
DOI: 10.16337/j.1004-9037.2023.05.013
Abstract:
Aiming at the continuous intelligent anti-jamming decision-making and high real-time requirements of radar in high-dynamic environment， this paper constructs a deep Q network （DQN） model for radar intelligent anti-jamming decision-making， and proposes a hardware decision acceleration architecture based on field programmable gate array(FPGA）. In this architecture， an on-chip access mode is designed for radar intelligent decision-making environment interaction to improve real-time performance， which simplifies the iterative process of continuous decision-making of the DQN agent through the on-chip quantitative storage and state iterative calculation for environment interaction. In the proposed architecture， both the parallel computing and pipeline control acceleration of agent deep neural network are adopted， which further improves the real-time performance of decision-making. Simulation and experimental results show that， on the premise of ensuring the accuracy of decision-making， the designed intelligent anti-jamming decision-making accelerator achieves a speedup of nearly 46 times in single decision-making and a speedup of nearly 84 times in continuous decision-making compared with the existing decision-making system based on the CPU platform.
Aiming at the continuous intelligent anti-jamming decision-making and high real-time requirements of radar in high-dynamic environment， this paper constructs a deep Q network （DQN） model for radar intelligent anti-jamming decision-making， and proposes a hardware decision acceleration architecture based on field programmable gate array(FPGA）. In this architecture， an on-chip access mode is designed for radar intelligent decision-making environment interaction to improve real-time performance， which simplifies the iterative process of continuous decision-making of the DQN agent through the on-chip quantitative storage and state iterative calculation for environment interaction. In the proposed architecture， both the parallel computing and pipeline control acceleration of agent deep neural network are adopted， which further improves the real-time performance of decision-making. Simulation and experimental results show that， on the premise of ensuring the accuracy of decision-making， the designed intelligent anti-jamming decision-making accelerator achieves a speedup of nearly 46 times in single decision-making and a speedup of nearly 84 times in continuous decision-making compared with the existing decision-making system based on the CPU platform.
2023, 38(1):51-62.
DOI: 10.16337/j.1004-9037.2023.01.003
Abstract:
How to quickly and accurately identify the key nodes in complex communication networks under a known network topology has become a hot spot in recent years. In this paper， we first establish the system model of weighted networks for key node identification . Then， a key node identification method based on weighted collective influence is proposed. In this method， the collective influence is used to quantify the information transmission ability of nodes， and the weighted idea is combined to represent the critical degree of weighted network nodes. Finally， five typical types of complex network models are simulated with random weight and non-random weight， respectively. Simulation results show that the proposed method outperforms the original collective influence algorithm， and the algorithm is not sensitive to the parameter of sphere radius.
How to quickly and accurately identify the key nodes in complex communication networks under a known network topology has become a hot spot in recent years. In this paper， we first establish the system model of weighted networks for key node identification . Then， a key node identification method based on weighted collective influence is proposed. In this method， the collective influence is used to quantify the information transmission ability of nodes， and the weighted idea is combined to represent the critical degree of weighted network nodes. Finally， five typical types of complex network models are simulated with random weight and non-random weight， respectively. Simulation results show that the proposed method outperforms the original collective influence algorithm， and the algorithm is not sensitive to the parameter of sphere radius.
2023, 38(4):986-994.
DOI: 10.16337/j.1004-9037.2023.04.020
Abstract:
With the rapid development of wireless communication technology， the number of wireless access devices is increasing while the energy consumption of the system is also increasing. An orthogonal frequency division multiplexing（OFDM） system with wireless energy-carrying communication capability can effectively improve energy efficiency. Aiming at the problem of resource allocation with system energy efficiency as the optimization goal， an energy efficiency optimization algorithm for energy-carrying communication OFDM systems based on ellipsoid method is proposed. The algorithm uses ellipsoid method to update the Lagrange multiplier， which can effectively accelerate the convergence speed and improve the performance of the algorithm. Simulation results show that the proposed algorithm can effectively solve the resource allocation problem with system energy efficiency as the optimization objective. Compared with the subgradient method， the ellipsoid method has a faster convergence speed and can significantly reduce the complexity of the algorithm.
With the rapid development of wireless communication technology， the number of wireless access devices is increasing while the energy consumption of the system is also increasing. An orthogonal frequency division multiplexing（OFDM） system with wireless energy-carrying communication capability can effectively improve energy efficiency. Aiming at the problem of resource allocation with system energy efficiency as the optimization goal， an energy efficiency optimization algorithm for energy-carrying communication OFDM systems based on ellipsoid method is proposed. The algorithm uses ellipsoid method to update the Lagrange multiplier， which can effectively accelerate the convergence speed and improve the performance of the algorithm. Simulation results show that the proposed algorithm can effectively solve the resource allocation problem with system energy efficiency as the optimization objective. Compared with the subgradient method， the ellipsoid method has a faster convergence speed and can significantly reduce the complexity of the algorithm.
2023, 38(6):1257-1267.
DOI: 10.16337/j.1004-9037.2023.06.002
Abstract:
For the problems of low degree of freedom and low accuracy in near-field source localization， a localization algorithm based on fourth-order cumulant matrix is proposed. Firstly， a high-dimensional virtual covariance matrix is constructed， where the equivalent steering vector contains both direction of arrival （DOA） and distance information. In angle estimation， a one-dimensional search method based on rank deficiency to search the reciprocal of the minimum singular value is proposed， where the computational burden is reduced. The degrees of freedom are increased and the characteristic that the high-order cumulant of Gaussian noise is zero is exploited to improve the estimation performance at low signal-to-noise ratio. In the estimation of distance， the distance information contained in the singular vector obtained by singular value decomposition in angle estimation can be directly exploited without additional calculation， and the distance is estimated by the least square method. Simulation results show that the method estimates the angle and distance information of the near-field source through the one-dimensional search only in a high-order cumulant matrix， which reduces the computational burden and improves the accuracy of the estimation compared with the existing algorithms. Moreover， the proposed method has twice as many degrees of freedom as the reduced-dimension MUSIC method.
For the problems of low degree of freedom and low accuracy in near-field source localization， a localization algorithm based on fourth-order cumulant matrix is proposed. Firstly， a high-dimensional virtual covariance matrix is constructed， where the equivalent steering vector contains both direction of arrival （DOA） and distance information. In angle estimation， a one-dimensional search method based on rank deficiency to search the reciprocal of the minimum singular value is proposed， where the computational burden is reduced. The degrees of freedom are increased and the characteristic that the high-order cumulant of Gaussian noise is zero is exploited to improve the estimation performance at low signal-to-noise ratio. In the estimation of distance， the distance information contained in the singular vector obtained by singular value decomposition in angle estimation can be directly exploited without additional calculation， and the distance is estimated by the least square method. Simulation results show that the method estimates the angle and distance information of the near-field source through the one-dimensional search only in a high-order cumulant matrix， which reduces the computational burden and improves the accuracy of the estimation compared with the existing algorithms. Moreover， the proposed method has twice as many degrees of freedom as the reduced-dimension MUSIC method.
2023, 38(6):1235-1256.
DOI: 10.16337/j.1004-9037.2023.06.001
Abstract:
The hot research topic of adversarial sample attacks on modulation recognition is reviewed. Firstly， we introduce the concepts and terms related to modulation recognition adversarial samples. Then we review and sort out the related research results on adversarial sample attacks and defense methods， and classify the existing adversarial attack methods and explain their generation mechanisms. Finally， based on the existing research， potential opportunities and challenges， and the advantages of artificial intelligence algorithms， the technical directions and development prospects of adversarial attacks in next-generation intelligent wireless communications are presented.
The hot research topic of adversarial sample attacks on modulation recognition is reviewed. Firstly， we introduce the concepts and terms related to modulation recognition adversarial samples. Then we review and sort out the related research results on adversarial sample attacks and defense methods， and classify the existing adversarial attack methods and explain their generation mechanisms. Finally， based on the existing research， potential opportunities and challenges， and the advantages of artificial intelligence algorithms， the technical directions and development prospects of adversarial attacks in next-generation intelligent wireless communications are presented.
2023, 38(3):506-524.
DOI: 10.16337/j.1004-9037.2023.03.002
Abstract:
Unmanned aerial vehicle（UAV） swarms have become critical equipment for performing complex tasks due to their flexibility， low cost， and the ability to carry various sensors. Their application depends on timely and efficient communication. Therefore， the research on UAV swarm communication networks has also received widespread attention in recent years. The inherent characteristics of UAV swarms， such as high mobility， high information interaction， and low energy storage， impose various severe challenges on the management of communication resources. This paper summarizes the application scenarios， advantages， and characteristics of the UAV swarm communication network， and extracts the challenges faced by resource optimization. From the perspectives of strategies and methods， this paper summarizes the existing resource optimization schemes， and sorts out the technical difficulties， such as communication performance improvement in large-scale cluster scenarios， timely decision update in high-complex environments， and communication satisfaction improvement in multi-heterogeneous requirements. Finally， the technical direction and development prospects of the UAV swarm communication network are prospected based on the research status， potential application value and the application advantages of emerging technologies.
Unmanned aerial vehicle（UAV） swarms have become critical equipment for performing complex tasks due to their flexibility， low cost， and the ability to carry various sensors. Their application depends on timely and efficient communication. Therefore， the research on UAV swarm communication networks has also received widespread attention in recent years. The inherent characteristics of UAV swarms， such as high mobility， high information interaction， and low energy storage， impose various severe challenges on the management of communication resources. This paper summarizes the application scenarios， advantages， and characteristics of the UAV swarm communication network， and extracts the challenges faced by resource optimization. From the perspectives of strategies and methods， this paper summarizes the existing resource optimization schemes， and sorts out the technical difficulties， such as communication performance improvement in large-scale cluster scenarios， timely decision update in high-complex environments， and communication satisfaction improvement in multi-heterogeneous requirements. Finally， the technical direction and development prospects of the UAV swarm communication network are prospected based on the research status， potential application value and the application advantages of emerging technologies.
2023, 38(6):1268-1275.
DOI: 10.16337/j.1004-9037.2023.06.003
Abstract:
The trade-off between control accuracy， bandwidth， and transmission rate in control systems is an important open question. This paper studies the trade-off relation between information transmission rate and distortion from the perspective of information theory. The actuator part of the control system is analyzed based on rate entropy function. An entropy rate analysis framework for a class of reproducible probability distribution function is established. The derived entropy rate inequality represents the relationship between posterior entropy and desired transmission rate in the finitre impulse rsponse（FIR） actuator. Based on the derived expressions， the mutual information data of the tracking system under different signal-to-noise ratios is simulated， and the simulation results are coordinated with the expression expectations.
The trade-off between control accuracy， bandwidth， and transmission rate in control systems is an important open question. This paper studies the trade-off relation between information transmission rate and distortion from the perspective of information theory. The actuator part of the control system is analyzed based on rate entropy function. An entropy rate analysis framework for a class of reproducible probability distribution function is established. The derived entropy rate inequality represents the relationship between posterior entropy and desired transmission rate in the finitre impulse rsponse（FIR） actuator. Based on the derived expressions， the mutual information data of the tracking system under different signal-to-noise ratios is simulated， and the simulation results are coordinated with the expression expectations.
2023, 38(1):63-73.
DOI: 10.16337/j.1004-9037.2023.01.004
Abstract:
A shortwave wideband specific signal detection algorithm based on frequency-sensitive attention is proposed to improve the accuracy of specific signal detection and recognition in a shortwave complex electromagnetic environment. A frequency-sensitive attention mechanism with a narrow and long shape receptive field is designed based on the correlation in the time direction and the locality in the frequency direction of shortwave specific signals in the spectrogram， and an end-to-end shortwave specific signal detector frequency sensitive signal detector （FSSDet） is constructed on this basis by segmenting the feature map into strip block along the time-axis direction and calculating the self-attention in the strip block， capturing the long-distance dependence in time-axis direction and limiting the sensing range in frequency-axis direction. FSSDet can directly output the modulation type of several specific signals， as well as important parameter information such as start and end time， center frequency， and bandwidth when a spectrogram of a shortwave wideband signal is given as input. Experiments are carried out on a simulation dataset of 47 880 samples from eight classes， and the proposed method has mean average precision （mAP） as high as 98.5 above 0 dB and remains above 72.5 when the signal noise ratio （SNR） is as low as -10 dB. The results show that the proposed method detects and recognizes short wave specific signals with high accuracy and robustness under low SNR.
A shortwave wideband specific signal detection algorithm based on frequency-sensitive attention is proposed to improve the accuracy of specific signal detection and recognition in a shortwave complex electromagnetic environment. A frequency-sensitive attention mechanism with a narrow and long shape receptive field is designed based on the correlation in the time direction and the locality in the frequency direction of shortwave specific signals in the spectrogram， and an end-to-end shortwave specific signal detector frequency sensitive signal detector （FSSDet） is constructed on this basis by segmenting the feature map into strip block along the time-axis direction and calculating the self-attention in the strip block， capturing the long-distance dependence in time-axis direction and limiting the sensing range in frequency-axis direction. FSSDet can directly output the modulation type of several specific signals， as well as important parameter information such as start and end time， center frequency， and bandwidth when a spectrogram of a shortwave wideband signal is given as input. Experiments are carried out on a simulation dataset of 47 880 samples from eight classes， and the proposed method has mean average precision （mAP） as high as 98.5 above 0 dB and remains above 72.5 when the signal noise ratio （SNR） is as low as -10 dB. The results show that the proposed method detects and recognizes short wave specific signals with high accuracy and robustness under low SNR.
2023, 38(1):74-84.
DOI: 10.16337/j.1004-9037.2023.01.005
Abstract:
Power quality has always attracted attention. The number of power electronic equipments in the power system and harmonics generated are increasing. The problem of harmonics has always been a topic of concern. This paper proposes a frequency estimation algorithm for power system harmonics and inter-harmonics by introducing the compressed sensing theory and the parallel factor model. First， this paper obtains the data at the signal receiving end， uses Euler’s formula to convert the sine signal into a spatial signal， and constructs the multi-delay output into a parallel factor model. Second， we compress the three slices of the model， and use the trilinear alternating least squares algorithm parallel factorization（PARAFAC）. Finally， the obtained data is sparsely reconstructed to obtain the frequency of the automatic pairing. Compared with the traditional parallel factor algorithm， this method has a compression process， a minor calculation， and lower storage capacity requirements. The frequency estimation performance of the proposed algorithm is very similar to that of the traditional PARAFAC method and better than that of the estimating signal parameter via rotational invariance techniques （ESPRIT） method.
Power quality has always attracted attention. The number of power electronic equipments in the power system and harmonics generated are increasing. The problem of harmonics has always been a topic of concern. This paper proposes a frequency estimation algorithm for power system harmonics and inter-harmonics by introducing the compressed sensing theory and the parallel factor model. First， this paper obtains the data at the signal receiving end， uses Euler’s formula to convert the sine signal into a spatial signal， and constructs the multi-delay output into a parallel factor model. Second， we compress the three slices of the model， and use the trilinear alternating least squares algorithm parallel factorization（PARAFAC）. Finally， the obtained data is sparsely reconstructed to obtain the frequency of the automatic pairing. Compared with the traditional parallel factor algorithm， this method has a compression process， a minor calculation， and lower storage capacity requirements. The frequency estimation performance of the proposed algorithm is very similar to that of the traditional PARAFAC method and better than that of the estimating signal parameter via rotational invariance techniques （ESPRIT） method.
2023, 38(1):193-208.
DOI: 10.16337/j.1004-9037.2023.01.017
Abstract:
Sensitivity encoding （SENSE） is a widely used parallel magnetic resonance imaging （MRI） reconstruction model. Many improved models have been proposed to improve the reconstruction performance of SENSE. However， the reconstructed images of these improved methods still have many artifacts. Especially， it is difficult to reconstruct a clearer image when the acceleration factor is higher. Therefore， based on nonlocal low-rank（NLR） constraints， this paper proposes an improved SENSE model， named NLR-SENSE model， which can effectively improve the quality of parallel MRI reconstructed images. We adopt the weighted kernel norm as the rank surrogate function， and use the alternating direction multiplier method （ADMM） to solve the NLR-SENSE model. Simulation results show that， compared with several other parallel MRI reconstruction methods， the NLR-SENSE model performs better in visual comparison and three different objective metrics， and can effectively improve the quality of the reconstructed image.
Sensitivity encoding （SENSE） is a widely used parallel magnetic resonance imaging （MRI） reconstruction model. Many improved models have been proposed to improve the reconstruction performance of SENSE. However， the reconstructed images of these improved methods still have many artifacts. Especially， it is difficult to reconstruct a clearer image when the acceleration factor is higher. Therefore， based on nonlocal low-rank（NLR） constraints， this paper proposes an improved SENSE model， named NLR-SENSE model， which can effectively improve the quality of parallel MRI reconstructed images. We adopt the weighted kernel norm as the rank surrogate function， and use the alternating direction multiplier method （ADMM） to solve the NLR-SENSE model. Simulation results show that， compared with several other parallel MRI reconstruction methods， the NLR-SENSE model performs better in visual comparison and three different objective metrics， and can effectively improve the quality of the reconstructed image.
2023, 38(1):220-230.
DOI: 10.16337/j.1004-9037.2023.01.019
Abstract:
Pseudorange error is a key factor affecting the positioning accuracy of the BeiDou satellite navigation receiver. A two-stage pseudorange error compensation method based on the pseudorange difference and the adaptive cubature Kalman filter （CKF） for BeiDou navigation receiver is proposed in this paper. Pseudorange error is divided into the self error and the common error. Firstly， the self error is compensated with the pseudorange difference method. Secondly， the measure noise adaptive CKF filter is designed to estimate the state of the receiver moving system in order to compensate the common error. Experimental results show that the the two-stage compensation method is slightly better under static conditions. The two-stage compensation reduces the localization error significantly than the single-stage compensation when the carrier is dynamic， and the adaptive CKF algorithm has better adaptability to noise and interference than the CKF algorithm.
Pseudorange error is a key factor affecting the positioning accuracy of the BeiDou satellite navigation receiver. A two-stage pseudorange error compensation method based on the pseudorange difference and the adaptive cubature Kalman filter （CKF） for BeiDou navigation receiver is proposed in this paper. Pseudorange error is divided into the self error and the common error. Firstly， the self error is compensated with the pseudorange difference method. Secondly， the measure noise adaptive CKF filter is designed to estimate the state of the receiver moving system in order to compensate the common error. Experimental results show that the the two-stage compensation method is slightly better under static conditions. The two-stage compensation reduces the localization error significantly than the single-stage compensation when the carrier is dynamic， and the adaptive CKF algorithm has better adaptability to noise and interference than the CKF algorithm.
2023, 38(3):727-740.
DOI: 10.16337/j.1004-9037.2023.03.020
Abstract:
With the advent of the data era， the classification of unbalanced data is receiving more and more attention. In the classification of unbalanced data， classification results are often incorrect due to an imbalance in the ratio of minority class samples to majority class ones. Therefore， we propose an oversampling algorithm based on the Bootstrap method under the maximum entropy principle. Firstly， the probability distribution of the data sample is obtaited through self-help method and optimized using the principle of maximum entropy. Secondly， a probability enhancement algorithm based on minority class sample distribution is proposed based on different abilities of minority classes to generate new minority classes. The algorithm allows the randomness of the data to be fully represented and ensures that the probability density of the minority class remains consistent before and after the data set is balanced， thus improving the effectiveness of the classification algorithm. Finally， experiments are conducted by selecting eight data sets from the UCI and KEEL databases， whose results show that the proposed algorithm is more effective than other algorithms.
With the advent of the data era， the classification of unbalanced data is receiving more and more attention. In the classification of unbalanced data， classification results are often incorrect due to an imbalance in the ratio of minority class samples to majority class ones. Therefore， we propose an oversampling algorithm based on the Bootstrap method under the maximum entropy principle. Firstly， the probability distribution of the data sample is obtaited through self-help method and optimized using the principle of maximum entropy. Secondly， a probability enhancement algorithm based on minority class sample distribution is proposed based on different abilities of minority classes to generate new minority classes. The algorithm allows the randomness of the data to be fully represented and ensures that the probability density of the minority class remains consistent before and after the data set is balanced， thus improving the effectiveness of the classification algorithm. Finally， experiments are conducted by selecting eight data sets from the UCI and KEEL databases， whose results show that the proposed algorithm is more effective than other algorithms.
2023, 38(5):1172-1179.
DOI: 10.16337/j.1004-9037.2023.05.015
Abstract:
To speed up the antenna modeling and optimization， this paper conducts a modeling study for antenna parameter optimization by the commercially available antenna design software. Firstly， the back propagation（BP） neural networks are optimized by several commonly-used heuristic algorithms， and used to improve the antenna parameters. These parameters are compared and the best one is the one optimized by genetic algorithm BP （GABP）. Secondly， the adaptive algorithm and simulated annealing algorithm is used to optimize GABP. Finally， the minimum error of the adaptive GABP algorithm for antenna parameter optimization is verified by simulation tests. The study provides a new method for antenna optimization in antenna design software with less errors. It has higher prediction accuracy and much faster fitting speed. The feasibility of this algorithm is also demonstrated by experimental comparison.
To speed up the antenna modeling and optimization， this paper conducts a modeling study for antenna parameter optimization by the commercially available antenna design software. Firstly， the back propagation（BP） neural networks are optimized by several commonly-used heuristic algorithms， and used to improve the antenna parameters. These parameters are compared and the best one is the one optimized by genetic algorithm BP （GABP）. Secondly， the adaptive algorithm and simulated annealing algorithm is used to optimize GABP. Finally， the minimum error of the adaptive GABP algorithm for antenna parameter optimization is verified by simulation tests. The study provides a new method for antenna optimization in antenna design software with less errors. It has higher prediction accuracy and much faster fitting speed. The feasibility of this algorithm is also demonstrated by experimental comparison.
2023, 38(5):1162-1171.
DOI: 10.16337/j.1004-9037.2023.05.014
Abstract:
Intelligent reflecting surface （IRS） is one of the most attractive key techniques to realize smart radio environments. It is effective to mitigate Doppler effect in the high-mobility environments by deploying IRSs. An IRS-assisted Doppler mitigation method has been proposed for high-mobility communications in the literature. However， the computational complexity of IRS phase optimization is high due to the maximum likelihood estimator for partial channel parameters. A simplified IRS phase optimization method is proposed， and the phase expression is derived. The channel improved parameters can be obtained by a low-complexity channel estimation method. Compared with the other scheme， the new scheme avoids the complex estimation methods， prevents the additional estimation errors， and effectively reduces the computational complexity. Numerical simulation results show that the new scheme can effectively reduce the program running time， while still achieving superior passive beamforming gain and strong robustness when pilot overhead is limited.
Intelligent reflecting surface （IRS） is one of the most attractive key techniques to realize smart radio environments. It is effective to mitigate Doppler effect in the high-mobility environments by deploying IRSs. An IRS-assisted Doppler mitigation method has been proposed for high-mobility communications in the literature. However， the computational complexity of IRS phase optimization is high due to the maximum likelihood estimator for partial channel parameters. A simplified IRS phase optimization method is proposed， and the phase expression is derived. The channel improved parameters can be obtained by a low-complexity channel estimation method. Compared with the other scheme， the new scheme avoids the complex estimation methods， prevents the additional estimation errors， and effectively reduces the computational complexity. Numerical simulation results show that the new scheme can effectively reduce the program running time， while still achieving superior passive beamforming gain and strong robustness when pilot overhead is limited.
2023, 38(6):1380-1391.
DOI: 10.16337/j.1004-9037.2023.06.013
Abstract:
To meet the fast imaging requirements of long-orbit ultra-high resolution W-band synthetic aperture radar（SAR）， this paper proposes a graphics processing unit（GPU）-based ω-K real-time imaging algorithm which adopts parallel architecture and double stream multithreading processing. The default stream processes data along the direction of the physical principle. Firstly， it parallelizes the rang compensation， error correction， zero filling and other operations， and then adopts one-layer nested interpolation method. By maintaining the upper and lower dependencies and synchronization management， it can achieve a speed ratio of about 30. The blocking stream starts at the same time as the default stream， generates the parameters and functions required by the default stream， and stores them into video memory before execution， which can greatly reduce the running time of the algorithm. Meanwhile， by setting events on the default stream， the two streams can be executed synchronously in parallel. Experimental results show that the total acceleration ratio of the algorithm can reach about 13， and the relative errors of amplitude and phase are close to 0， which not only has good real-time performance and focusing performance， but also maintains good imaging effect.
To meet the fast imaging requirements of long-orbit ultra-high resolution W-band synthetic aperture radar（SAR）， this paper proposes a graphics processing unit（GPU）-based ω-K real-time imaging algorithm which adopts parallel architecture and double stream multithreading processing. The default stream processes data along the direction of the physical principle. Firstly， it parallelizes the rang compensation， error correction， zero filling and other operations， and then adopts one-layer nested interpolation method. By maintaining the upper and lower dependencies and synchronization management， it can achieve a speed ratio of about 30. The blocking stream starts at the same time as the default stream， generates the parameters and functions required by the default stream， and stores them into video memory before execution， which can greatly reduce the running time of the algorithm. Meanwhile， by setting events on the default stream， the two streams can be executed synchronously in parallel. Experimental results show that the total acceleration ratio of the algorithm can reach about 13， and the relative errors of amplitude and phase are close to 0， which not only has good real-time performance and focusing performance， but also maintains good imaging effect.
2023, 38(6):1353-1368.
DOI: 10.16337/j.1004-9037.2023.06.011
Abstract:
With the growth of Internet video traffic and the improvement of user requirements for experience quality， the traditional backbone network is facing great pressure. Moving edge cache technology can reduce latency， reduce backhaul link load， and improve video user experience quality. However， the finiteness of edge server cache resources， the dynamic nature of video requests， and the attention of users to the security of cached data pose new challenges to the research of edge cache strategy. To solve the above problems， this paper proposes a blockchain-assisted multi-edge server collaborative video stream cache optimization scheme. This paper constructs a four-layer network architecture composed of content delivery network （CDN） server， edge server， user device， and blockchain. We introduce the blockchain consensus mechanism to protect the charging video with insensitive request delay and ensure the data security of users. Based on the three-layer cache mechanism of local hit， proximity hit and CDN hit， the collaborative cache among edge servers is further strengthened by defining proximity hit reward factors， and the cache hit ratio of edge servers is improved. In this paper， we jointly consider the state of edge servers， the change in content popularity， and the resource allocation of cooperative cache among multi-edge servers. By establishing the minimum access latency， traffic cost， and system energy consumption optimization problem. The cooperative cache optimization algorithm of multi-agent proximal policy optimization （MAPPO） is used to solve the problem. The simulation results show that compared with the existing caching strategies， the proposed scheme can effectively improve the cache hit rate of video streaming and reduce energy consumption and delay.
With the growth of Internet video traffic and the improvement of user requirements for experience quality， the traditional backbone network is facing great pressure. Moving edge cache technology can reduce latency， reduce backhaul link load， and improve video user experience quality. However， the finiteness of edge server cache resources， the dynamic nature of video requests， and the attention of users to the security of cached data pose new challenges to the research of edge cache strategy. To solve the above problems， this paper proposes a blockchain-assisted multi-edge server collaborative video stream cache optimization scheme. This paper constructs a four-layer network architecture composed of content delivery network （CDN） server， edge server， user device， and blockchain. We introduce the blockchain consensus mechanism to protect the charging video with insensitive request delay and ensure the data security of users. Based on the three-layer cache mechanism of local hit， proximity hit and CDN hit， the collaborative cache among edge servers is further strengthened by defining proximity hit reward factors， and the cache hit ratio of edge servers is improved. In this paper， we jointly consider the state of edge servers， the change in content popularity， and the resource allocation of cooperative cache among multi-edge servers. By establishing the minimum access latency， traffic cost， and system energy consumption optimization problem. The cooperative cache optimization algorithm of multi-agent proximal policy optimization （MAPPO） is used to solve the problem. The simulation results show that compared with the existing caching strategies， the proposed scheme can effectively improve the cache hit rate of video streaming and reduce energy consumption and delay.
2023, 38(6):1342-1352.
DOI: 10.16337/j.1004-9037.2023.06.010
Abstract:
Spectrum sensing under α noise has become a hot topic in recent years. The statistical model of this noise has obvious impulse and trailing characteristics. The signal characteristics are not obvious enough under weak signal conditions. To this end， the maximum generalized correntropy spectrum sensing method based on stochastic resonance is proposed. This method uses the transition of particles in the stochastic resonance model between the two potential wells to transfer part of the energy of alpha noise into the signal to improve the signal output signal-to-noise ratio. The maximum generalized correntropy method is utilized to construct high-order statistics for spectrum sensing， detect the output signal after stochastic resonance and combine conjugate gradient descent method to achieve the optimal objective function. The simulations results demonstrate that the proposed algorithm can effectively improve the detection performance under the condition of low signal-to-noise ratio.
Spectrum sensing under α noise has become a hot topic in recent years. The statistical model of this noise has obvious impulse and trailing characteristics. The signal characteristics are not obvious enough under weak signal conditions. To this end， the maximum generalized correntropy spectrum sensing method based on stochastic resonance is proposed. This method uses the transition of particles in the stochastic resonance model between the two potential wells to transfer part of the energy of alpha noise into the signal to improve the signal output signal-to-noise ratio. The maximum generalized correntropy method is utilized to construct high-order statistics for spectrum sensing， detect the output signal after stochastic resonance and combine conjugate gradient descent method to achieve the optimal objective function. The simulations results demonstrate that the proposed algorithm can effectively improve the detection performance under the condition of low signal-to-noise ratio.
2023, 38(6):1331-1341.
DOI: 10.16337/j.1004-9037.2023.06.009
Abstract:
In order to increase the bits of binary data transmitted per second in direct sequence spread spectrum（DSSS） systems and enhance the security of information transmission， a mapping transmission mechanism is established， and a two-dimensional data transmission method with constellation rotation mapping is proposed. As the one-dimensional data is transmitted， the two-dimensional data is added， and the relationship model is established by using the M-ary conversion and constellation rotation. The constellation is selected according to the ratio between one-dimensional data rate and two-dimensional data rate， and then the two-dimensional data is converted into mapping data by constellation rotation mapping， so as to obtain the corresponding pseudo-code channel and achieve the transmission of one-dimensional data and the mapping transmission of two-dimensional data. The simulation results show that compared with the traditional DSSS system， the two-dimensional data transmission method with constellation rotation mapping can obtain higher data transmission rate and better error code rate performance， as well as meet the requirements of better confidentiality performance.
In order to increase the bits of binary data transmitted per second in direct sequence spread spectrum（DSSS） systems and enhance the security of information transmission， a mapping transmission mechanism is established， and a two-dimensional data transmission method with constellation rotation mapping is proposed. As the one-dimensional data is transmitted， the two-dimensional data is added， and the relationship model is established by using the M-ary conversion and constellation rotation. The constellation is selected according to the ratio between one-dimensional data rate and two-dimensional data rate， and then the two-dimensional data is converted into mapping data by constellation rotation mapping， so as to obtain the corresponding pseudo-code channel and achieve the transmission of one-dimensional data and the mapping transmission of two-dimensional data. The simulation results show that compared with the traditional DSSS system， the two-dimensional data transmission method with constellation rotation mapping can obtain higher data transmission rate and better error code rate performance， as well as meet the requirements of better confidentiality performance.
42 A Space-Frequency Anti-jamming Algorithm Based on Variable Step LMS of Tongue-Like Curve Function
2023, 38(6):1319-1330.
DOI: 10.16337/j.1004-9037.2023.06.008
Abstract:
To solve the problem that the space-frequency algorithm based on least mean square （LMS） cannot consider the anti-jamming performance and the convergence speed simultaneously， a space-frequency anti-jamming algorithm based on variable step LMS of tongue-like curve function is proposed as space-frequency variable step LMS of tongue-like curve function（TLCVSLMS） algorithm. On the basis of both anti-interference performance and convergence speed， the space frequency TLCVSLMS algorithm avoids the difficulty of artificially selecting a suitable fixed iterative step size factor for each frequency point， and makes more precise adjustments to the amplitude factor and shape factor of the tongue line function based on the signal power of different frequency points. Simulation results show that， when the anti-interference performance is close， the space-frequency TLCVSLMS algorithm has at least 400 fewer iterations than the space-frequency LMS algorithm， and the convergence speed of the space-frequency TLCVSLMS algorithm is faster. When the convergence speed is proximate， the anti-interference performance of the space-frequency TLCVSLMS algorithm is improved at least 3 dB than the space-frequency LMS algorithm.
To solve the problem that the space-frequency algorithm based on least mean square （LMS） cannot consider the anti-jamming performance and the convergence speed simultaneously， a space-frequency anti-jamming algorithm based on variable step LMS of tongue-like curve function is proposed as space-frequency variable step LMS of tongue-like curve function（TLCVSLMS） algorithm. On the basis of both anti-interference performance and convergence speed， the space frequency TLCVSLMS algorithm avoids the difficulty of artificially selecting a suitable fixed iterative step size factor for each frequency point， and makes more precise adjustments to the amplitude factor and shape factor of the tongue line function based on the signal power of different frequency points. Simulation results show that， when the anti-interference performance is close， the space-frequency TLCVSLMS algorithm has at least 400 fewer iterations than the space-frequency LMS algorithm， and the convergence speed of the space-frequency TLCVSLMS algorithm is faster. When the convergence speed is proximate， the anti-interference performance of the space-frequency TLCVSLMS algorithm is improved at least 3 dB than the space-frequency LMS algorithm.
2023, 38(6):1307-1318.
DOI: 10.16337/j.1004-9037.2023.06.007
Abstract:
Finite impulse response （FIR） filters are generally used in the design of traditional polynomial broadband beamformers. A generalized polynomial broadband beamformer is proposed by using the orthogonal basis filter instead of FIR filter of the traditional design in this paper. The proposed polynomial broadband beamformer can be regarded as an extension of the traditional polynomial broadband beamformers， so its structure is more flexible. In order to enhance the robustness of the generalized polynomial broadband beamformer， a robust optimization design method based on average performance optimization criterion is further proposed. The poles of the orthogonal basis filter are optimized by particle swarm optimization in this method， so that the performance of the polynomial broadband beamformer can be improved by using the freedom provided by the poles. By introducing the spatial derivative constraint of array response， the orientation deviation of beam mainlobe caused by finite polynomial interpolation is reduced. The simulation results show that， compared with the traditional design method of polynomial structure， the proposed design method can obtain better frequency invariant performance and robustness， effectively improve the orientation accuracy of beam mainlobe， and improve the directivity index of polynomial broadband beamformers.
Finite impulse response （FIR） filters are generally used in the design of traditional polynomial broadband beamformers. A generalized polynomial broadband beamformer is proposed by using the orthogonal basis filter instead of FIR filter of the traditional design in this paper. The proposed polynomial broadband beamformer can be regarded as an extension of the traditional polynomial broadband beamformers， so its structure is more flexible. In order to enhance the robustness of the generalized polynomial broadband beamformer， a robust optimization design method based on average performance optimization criterion is further proposed. The poles of the orthogonal basis filter are optimized by particle swarm optimization in this method， so that the performance of the polynomial broadband beamformer can be improved by using the freedom provided by the poles. By introducing the spatial derivative constraint of array response， the orientation deviation of beam mainlobe caused by finite polynomial interpolation is reduced. The simulation results show that， compared with the traditional design method of polynomial structure， the proposed design method can obtain better frequency invariant performance and robustness， effectively improve the orientation accuracy of beam mainlobe， and improve the directivity index of polynomial broadband beamformers.
2023, 38(6):1299-1306.
DOI: 10.16337/j.1004-9037.2023.06.006
Abstract:
The paper investigates the integration scheme of cell-free massive multiple-input multiple-output （MIMO） and non-orthogonal multiple access （NOMA）. NOMA based on group successive interference cancellation （GSIC） method is applied to the downlink cell-free massive MIMO systems. Furthermore， a novel grouping method according to user’s equivalent path loss is developed and the expression of per user’s achievable rate is derived. The simulation results show that the performance of downlink cell-free massive MIMO-NOMA systems based on GSIC is better than that of the cell-free massive MIMO-NOMA systems based on successive interference cancellation（SIC） in terms of achievable rate.
The paper investigates the integration scheme of cell-free massive multiple-input multiple-output （MIMO） and non-orthogonal multiple access （NOMA）. NOMA based on group successive interference cancellation （GSIC） method is applied to the downlink cell-free massive MIMO systems. Furthermore， a novel grouping method according to user’s equivalent path loss is developed and the expression of per user’s achievable rate is derived. The simulation results show that the performance of downlink cell-free massive MIMO-NOMA systems based on GSIC is better than that of the cell-free massive MIMO-NOMA systems based on successive interference cancellation（SIC） in terms of achievable rate.
2023, 38(6):1286-1298.
DOI: 10.16337/j.1004-9037.2023.06.005
Abstract:
In order to solve the problems of high cost， poor mobility and difficulty in coping with emergency in large-scale deployment of fixed edge computing nodes， a computing task offloading algorithm based on deep reinforcement learning is proposed to meet the needs of computing-intensive and delay-sensitive mobile services. Considering constraints such as the flight range， flight speed and system fairness benefits of multiple unmanned aerial vehicles （UAVs）， the method aims to minimize the weighted sum of the average computing delay of the network and the UAV energy consumption. This non-convex and non-deterministic polynomial（NP）-hard problem is transformed into a partially observed Markov decision process， and a multi-agent deep deterministic policy gradient algorithm is used for mobile user offloading decision and UAV flight trajectory optimization. Simulation results show that the proposed algorithm outperforms the baseline algorithm in terms of fairness of mobile service terminals， average system delay and total energy consumption of multiple UAVs. Especially， the proposed algorithm can obtain the optimal power consumption control under different computing performance. When the CPU frequency is 12.5 GHz， the energy consumption is 29.16% lower than the Cruise algorithm， and 8.67% lower than the advantage actor-critic（A2C） algorithm.
In order to solve the problems of high cost， poor mobility and difficulty in coping with emergency in large-scale deployment of fixed edge computing nodes， a computing task offloading algorithm based on deep reinforcement learning is proposed to meet the needs of computing-intensive and delay-sensitive mobile services. Considering constraints such as the flight range， flight speed and system fairness benefits of multiple unmanned aerial vehicles （UAVs）， the method aims to minimize the weighted sum of the average computing delay of the network and the UAV energy consumption. This non-convex and non-deterministic polynomial（NP）-hard problem is transformed into a partially observed Markov decision process， and a multi-agent deep deterministic policy gradient algorithm is used for mobile user offloading decision and UAV flight trajectory optimization. Simulation results show that the proposed algorithm outperforms the baseline algorithm in terms of fairness of mobile service terminals， average system delay and total energy consumption of multiple UAVs. Especially， the proposed algorithm can obtain the optimal power consumption control under different computing performance. When the CPU frequency is 12.5 GHz， the energy consumption is 29.16% lower than the Cruise algorithm， and 8.67% lower than the advantage actor-critic（A2C） algorithm.
2023, 38(3):690-703.
DOI: 10.16337/j.1004-9037.2023.03.017
Abstract:
Aiming at the shortcomings of slow convergence speed， easy to fall into local optimum and low convergence accuracy of basic harmony search （HS） algorithm， an improved HS（IHS） algorithm is proposed by combining sine cosine optimization operator， Levy flight mechanism and parameter dynamic adjustment strategy. In the improvisation stage， the algorithm first introduces a combination of sine cosine optimization operator and fine-tuning bandwidth to fine-tune the harmony vectors， makes full use of the position information of the optimal individual and the current individual， and improves the calculation accuracy and convergence speed of the algorithm.The Levy flight mechanism is then used to update the fine-tuned bandwidth to avoid the algorithm falling into local optimization and improve the global search capability. During the algorithm iteration process， adaptive dynamic adjustments are made to the storage probability， base tone fine-tuning probability and search domain of the harmony memory to further improve the convergence performance of the algorithm. The results of the performance test comparison experiment on ten reference functions show that the proposed algorithm has the stronger global search ability， the faster convergence speed and the better calculation accuracy.
Aiming at the shortcomings of slow convergence speed， easy to fall into local optimum and low convergence accuracy of basic harmony search （HS） algorithm， an improved HS（IHS） algorithm is proposed by combining sine cosine optimization operator， Levy flight mechanism and parameter dynamic adjustment strategy. In the improvisation stage， the algorithm first introduces a combination of sine cosine optimization operator and fine-tuning bandwidth to fine-tune the harmony vectors， makes full use of the position information of the optimal individual and the current individual， and improves the calculation accuracy and convergence speed of the algorithm.The Levy flight mechanism is then used to update the fine-tuned bandwidth to avoid the algorithm falling into local optimization and improve the global search capability. During the algorithm iteration process， adaptive dynamic adjustments are made to the storage probability， base tone fine-tuning probability and search domain of the harmony memory to further improve the convergence performance of the algorithm. The results of the performance test comparison experiment on ten reference functions show that the proposed algorithm has the stronger global search ability， the faster convergence speed and the better calculation accuracy.
2023, 38(3):608-615.
DOI: 10.16337/j.1004-9037.2023.03.010
Abstract:
Long-term detection and evaluation of electrocardiogram （ECG） signals is crucial for the diagnosis and prevention of cardiovascular disease. However， the detection of ECG signals usually needs to install electrodes on the patient， which can easily cause discomfort to the subject， and the scope of application is thus limited. In contrast， pulse wave signals detected by photoplethysmography （PPG） not only contains rich cardiovascular physiological and pathological information， but also is easy to be measured. Considering the inherent mapping relationship between PPG and ECG signals， a model of transferring PPG to ECG signals based on generative adversarial network （GAN） is proposed. The generator network is composed of the Unet model， the structure of Unet++ is referenced in the feature map fusion， and the discriminator network is composed of a convolutional neural network. During the training process， gradient penalty is utilized to increase the stability of the model. The experiment is conducted based on public datasets. By comparing the processing results of a sample of 53 subjects， the root mean square error （RMSE）， Pearson correlation coefficient （ρ） and Fréchet distance （FD） of the ECG signal generated by the new model are improved by 3.4%， 5.5% and 0.4%， respectively， proving that the new model has better PPG-ECG transfer effect.
Long-term detection and evaluation of electrocardiogram （ECG） signals is crucial for the diagnosis and prevention of cardiovascular disease. However， the detection of ECG signals usually needs to install electrodes on the patient， which can easily cause discomfort to the subject， and the scope of application is thus limited. In contrast， pulse wave signals detected by photoplethysmography （PPG） not only contains rich cardiovascular physiological and pathological information， but also is easy to be measured. Considering the inherent mapping relationship between PPG and ECG signals， a model of transferring PPG to ECG signals based on generative adversarial network （GAN） is proposed. The generator network is composed of the Unet model， the structure of Unet++ is referenced in the feature map fusion， and the discriminator network is composed of a convolutional neural network. During the training process， gradient penalty is utilized to increase the stability of the model. The experiment is conducted based on public datasets. By comparing the processing results of a sample of 53 subjects， the root mean square error （RMSE）， Pearson correlation coefficient （ρ） and Fréchet distance （FD） of the ECG signal generated by the new model are improved by 3.4%， 5.5% and 0.4%， respectively， proving that the new model has better PPG-ECG transfer effect.
2023, 38(2):304-313.
DOI: 10.16337/j.1004-9037.2023.02.007
Abstract:
In order to solve the space-time discontinuity problem of unmanned aerial vehicle（UAV） non-stationary channel parameters， this paper proposes a non-stationary geometric stochastic model of UAV that supports three-dimensional motion and posture rotation， which is used to describe and simulate the real characteristics of UAV multiple-input multiple-output communication channel. Based on the concept of “accurate qualitative” in QuaDRiGa， the model update time-varying channel parameters based on the topological relation of receiver and transmitter， improve the channel power calculation method considering the probability of path birth and death， and introduce posture phase matrix to describe UAV posture rotation， so as to realize smooth evolution of UAV non-stationary channel parameters. The numerical simulation results show that the model proposed in this paper can ensure the smooth evolution of parameters such as power and angle， and the autocorrelation function of the output channel is non-stationary and significantly affected by UAV posture. The proposed model can be used in UAV communication system design and algorithm optimization.
In order to solve the space-time discontinuity problem of unmanned aerial vehicle（UAV） non-stationary channel parameters， this paper proposes a non-stationary geometric stochastic model of UAV that supports three-dimensional motion and posture rotation， which is used to describe and simulate the real characteristics of UAV multiple-input multiple-output communication channel. Based on the concept of “accurate qualitative” in QuaDRiGa， the model update time-varying channel parameters based on the topological relation of receiver and transmitter， improve the channel power calculation method considering the probability of path birth and death， and introduce posture phase matrix to describe UAV posture rotation， so as to realize smooth evolution of UAV non-stationary channel parameters. The numerical simulation results show that the model proposed in this paper can ensure the smooth evolution of parameters such as power and angle， and the autocorrelation function of the output channel is non-stationary and significantly affected by UAV posture. The proposed model can be used in UAV communication system design and algorithm optimization.
2023, 38(3):717-726.
DOI: 10.16337/j.1004-9037.2023.03.019
Abstract:
With the development of communication technology， increasingly higher communication frequency bands are adopted. However， the electromagnetic wave diffraction capability decreases with increasing frequency. New generation communication systems become more dependent on line-of-sight propagation. Frequent beam switching is required in complex mobile scenarios， which increases excessive system overhead and delay. To address this problem， a position information-assisted grid-based beam switching method is proposed. The feature that the optimal beam pair remains constant in the presence of line of sight（LOS） path is utilized. The grid beam one-to-one correspondence and coverage distribution structure are divided. A position-beam mapping table is establised. All the next switching points and switching information are predicted based on position information and motion speed. The simulation and analysis results show that the proposed method significantly improves the spectral efficiency of the system compared with the non-grid switching method， and the proposed square hexagonal grid switching performance is better than the square grid， and the beam switching probability is reduced by 50%.It guarantees the communication quality and verifies the rationality of the grid-based beam switching method assisted by the position information.
With the development of communication technology， increasingly higher communication frequency bands are adopted. However， the electromagnetic wave diffraction capability decreases with increasing frequency. New generation communication systems become more dependent on line-of-sight propagation. Frequent beam switching is required in complex mobile scenarios， which increases excessive system overhead and delay. To address this problem， a position information-assisted grid-based beam switching method is proposed. The feature that the optimal beam pair remains constant in the presence of line of sight（LOS） path is utilized. The grid beam one-to-one correspondence and coverage distribution structure are divided. A position-beam mapping table is establised. All the next switching points and switching information are predicted based on position information and motion speed. The simulation and analysis results show that the proposed method significantly improves the spectral efficiency of the system compared with the non-grid switching method， and the proposed square hexagonal grid switching performance is better than the square grid， and the beam switching probability is reduced by 50%.It guarantees the communication quality and verifies the rationality of the grid-based beam switching method assisted by the position information.
2023, 38(2):314-323.
DOI: 10.16337/j.1004-9037.2023.02.008
Abstract:
Data distribution service（DDS） is a reliable real-time data communication middleware standard. It is oriented to a distributed environment based on the publish/subscribe model. It has been widely used in various fields. However， there are few achievements in existing research involving DDS security technology. There are many security threats to the publishing and subscribing system in practice. In order to establish a flexible and reliable security mechanism to ensure the security of publishing and subscribing information， a data-centric access control scheme is proposed. On the basis of attribute encryption， the access tree structure is optimized， and the attribute trust mechanism is added in combination with the publishing and subscribing environment. Afterwards， the publicating and subscripting information is encrypted and matched by formulating attribute connection and authorization strategies， and a DDS access control model is established to control the interaction of information in the publicating and subscripting system and realize the safe distribution of data. The experimental verification shows the solution can deal with several security threats in DDS， guarantee the confidentiality of publishing and subscribing information， as well as realize the system’s access control to specific information， and publishers and subscribers do not need to share keys， reducing the overhead of key management.
Data distribution service（DDS） is a reliable real-time data communication middleware standard. It is oriented to a distributed environment based on the publish/subscribe model. It has been widely used in various fields. However， there are few achievements in existing research involving DDS security technology. There are many security threats to the publishing and subscribing system in practice. In order to establish a flexible and reliable security mechanism to ensure the security of publishing and subscribing information， a data-centric access control scheme is proposed. On the basis of attribute encryption， the access tree structure is optimized， and the attribute trust mechanism is added in combination with the publishing and subscribing environment. Afterwards， the publicating and subscripting information is encrypted and matched by formulating attribute connection and authorization strategies， and a DDS access control model is established to control the interaction of information in the publicating and subscripting system and realize the safe distribution of data. The experimental verification shows the solution can deal with several security threats in DDS， guarantee the confidentiality of publishing and subscribing information， as well as realize the system’s access control to specific information， and publishers and subscribers do not need to share keys， reducing the overhead of key management.
2024, 39(6):1345-1354.
DOI: 10.16337/j.1004-9037.2024.06.005
Abstract:
Although multi-station radar cooperation can effectively improve the anti-jamming ability by using multi-view detection and information fusion processing， it is difficult to meet the detection conditions in the actual scene， or it suffers from the risk of network destruction in practice. Therefore， it is still necessary to improve the anti-jamming ability of single station radar. Aiming at problems of single-station radar， such as single detection angle， limited echo information and insufficient anti-jamming ability， a distributed detection condition is constructed by adding a reconfigurable intelligence surface （RIS） in the echo receiving process of single-station radar to receive the multi-directional scattering signal of the target， thus opening up a new way for the single-station radar to resist deceptive jamming. Simulation results show that adding RIS can effectively construct virtual channel and improve the anti-jamming ability of single station radar.
Although multi-station radar cooperation can effectively improve the anti-jamming ability by using multi-view detection and information fusion processing， it is difficult to meet the detection conditions in the actual scene， or it suffers from the risk of network destruction in practice. Therefore， it is still necessary to improve the anti-jamming ability of single station radar. Aiming at problems of single-station radar， such as single detection angle， limited echo information and insufficient anti-jamming ability， a distributed detection condition is constructed by adding a reconfigurable intelligence surface （RIS） in the echo receiving process of single-station radar to receive the multi-directional scattering signal of the target， thus opening up a new way for the single-station radar to resist deceptive jamming. Simulation results show that adding RIS can effectively construct virtual channel and improve the anti-jamming ability of single station radar.
2024, 39(6):1355-1369.
DOI: 10.16337/j.1004-9037.2024.06.006
Abstract:
To cope with the challenges brought by increasingly intelligent multifunctional radars to the opposing side， this paper proposes an jamming decision-making method based on the proximal policy optimization （PPO） algorithm and the Mask-Transformer in Transformer （Mask-TIT） network. Firstly， starting from a realistic scenario， the adversarial scene between the jammer and the radar is modeled as a partially observable Markov decision process （POMDP）. A new state transition function and reward function are designed based on the working principles of the radar， and the observation space is designed according to the hierarchy of the multifunctional radar model. Secondly， a Mask-TIT network structure is designed using the Transformer’s representation capacity for sequence data and the characteristics of radar jamming patterns， which is used to build a more powerful Actor-Critic network architecture. Finally， the PPO algorithm is used for optimization learning. Experimental results show that compared with existing methods， the proposed algorithm reduces the average amount of interactive data required for convergence by 25.6%， and the variance after convergence is significantly reduced.
To cope with the challenges brought by increasingly intelligent multifunctional radars to the opposing side， this paper proposes an jamming decision-making method based on the proximal policy optimization （PPO） algorithm and the Mask-Transformer in Transformer （Mask-TIT） network. Firstly， starting from a realistic scenario， the adversarial scene between the jammer and the radar is modeled as a partially observable Markov decision process （POMDP）. A new state transition function and reward function are designed based on the working principles of the radar， and the observation space is designed according to the hierarchy of the multifunctional radar model. Secondly， a Mask-TIT network structure is designed using the Transformer’s representation capacity for sequence data and the characteristics of radar jamming patterns， which is used to build a more powerful Actor-Critic network architecture. Finally， the PPO algorithm is used for optimization learning. Experimental results show that compared with existing methods， the proposed algorithm reduces the average amount of interactive data required for convergence by 25.6%， and the variance after convergence is significantly reduced.
2024, 39(1):106-119.
DOI: 10.16337/j.1004-9037.2024.01.010
Abstract:
Deep learning techniques have greatly improved the classification accuracy of synthetic aperture radar (SAR） images target， but the security of SAR image classification systems is threatened by the inherent vulnerability of neural networks. In this paper， we analyze the aggressiveness of SAR adversarial samples， and the difference between SAR adversarial examples and original examples in the frequency domain. With the analysis results， a two-step SAR adversarial samples detection technique is proposed to improve the security of SAR classification models. The first step of adversarial sample detection is performed on the input image based on the frequency domain analysis to separate the adversarial samples. Then， the remaining images are fed into an adversarial trained model and an untrained model to complete the second step of adversarial sample detection. By using this two-step detection method， the adversarial samples can be effectively detected with a detection success rate of no less than 95.73%， effectively improving the security of the SAR classification model.
Deep learning techniques have greatly improved the classification accuracy of synthetic aperture radar (SAR） images target， but the security of SAR image classification systems is threatened by the inherent vulnerability of neural networks. In this paper， we analyze the aggressiveness of SAR adversarial samples， and the difference between SAR adversarial examples and original examples in the frequency domain. With the analysis results， a two-step SAR adversarial samples detection technique is proposed to improve the security of SAR classification models. The first step of adversarial sample detection is performed on the input image based on the frequency domain analysis to separate the adversarial samples. Then， the remaining images are fed into an adversarial trained model and an untrained model to complete the second step of adversarial sample detection. By using this two-step detection method， the adversarial samples can be effectively detected with a detection success rate of no less than 95.73%， effectively improving the security of the SAR classification model.
2024, 39(6):1326-1332.
DOI: 10.16337/j.1004-9037.2024.06.003
Abstract:
The maximum a posteriori （MAP） algorithm is the most commonly used parameter estimation method. However， the MAP algorithm focuses on the position of the maximum peak of the posterior distribution and does not fully utilize the complete information of the posterior distribution. This article proposes a minimum divergence （MD） radar ranging estimation method based on relative entropy. Firstly， the posterior distribution of the parameters is derived. Secondly， a distribution similar to them is constructed. Therefore， the value is estimated by finding the minimum value of their divergence. Simulation results indicate that in radar ranging scenarios， the MD algorithm achieves approximately 1 dB gain in performance compared to the MAP algorithm， demonstrating its superior estimation performance.
The maximum a posteriori （MAP） algorithm is the most commonly used parameter estimation method. However， the MAP algorithm focuses on the position of the maximum peak of the posterior distribution and does not fully utilize the complete information of the posterior distribution. This article proposes a minimum divergence （MD） radar ranging estimation method based on relative entropy. Firstly， the posterior distribution of the parameters is derived. Secondly， a distribution similar to them is constructed. Therefore， the value is estimated by finding the minimum value of their divergence. Simulation results indicate that in radar ranging scenarios， the MD algorithm achieves approximately 1 dB gain in performance compared to the MAP algorithm， demonstrating its superior estimation performance.
2024, 39(6):1333-1344.
DOI: 10.16337/j.1004-9037.2024.06.004
Abstract:
Most existing electromagnetic vector sensor multiple-input multiple-output（EMVS-MIMO） radars restrict the distribution of transceiver array elements. The resolution of radar direction measurement is limited due to half-wavelength constraints. To address this limitation， this paper proposes an algorithm based on the parallel factor （PARAFAC） decomposition for two dimension （2D） angle estimation of the target. The algorithm is applicable to arbitrary transmitter array geometries and sparse receiver array geometries. First， a third-order PARAFAC tensor model is constructed for the matched-filtered signal of the receiving array. Second， the PARAFAC decomposition is utilized to estimate the transmit direction， receive direction， and composite factor matrix. Finally， a closed-form solution for high-resolution， ambiguity-free 2D angle estimation of the target is obtained by combining the rotationally invariant method， the vector outer product method and the least squares method. The proposed algorithm is characterized by high accuracy and low computational complexity. Computer simulations verify the tensor decomposition-based algorithm can be applied to an arbitrary dual-base EMVS-MIMO radar model， and can accurately estimate the 2D angular parameters of multiple targets. This validation demonstrates the effectiveness and superiority of the proposed algorithm.
Most existing electromagnetic vector sensor multiple-input multiple-output（EMVS-MIMO） radars restrict the distribution of transceiver array elements. The resolution of radar direction measurement is limited due to half-wavelength constraints. To address this limitation， this paper proposes an algorithm based on the parallel factor （PARAFAC） decomposition for two dimension （2D） angle estimation of the target. The algorithm is applicable to arbitrary transmitter array geometries and sparse receiver array geometries. First， a third-order PARAFAC tensor model is constructed for the matched-filtered signal of the receiving array. Second， the PARAFAC decomposition is utilized to estimate the transmit direction， receive direction， and composite factor matrix. Finally， a closed-form solution for high-resolution， ambiguity-free 2D angle estimation of the target is obtained by combining the rotationally invariant method， the vector outer product method and the least squares method. The proposed algorithm is characterized by high accuracy and low computational complexity. Computer simulations verify the tensor decomposition-based algorithm can be applied to an arbitrary dual-base EMVS-MIMO radar model， and can accurately estimate the 2D angular parameters of multiple targets. This validation demonstrates the effectiveness and superiority of the proposed algorithm.
2024, 39(1):132-139.
DOI: 10.16337/j.1004-9037.2024.01.012
Abstract:
This paper presents a roll angle calibration method for phased-array radar. Based on the built-in elevation calibration methods of phased-array radar， the proposed method utilizes the approximately linear relationship between the roll angle and the target’s elevation measurement error to calculate the roll angle. Experiments confirm that the roll angle calculated by this method has nearly the same accuracy as that of the method using laser measurement instruments. With the proposed method， the angle measurement accuracy of phased-array radar can be greatly improved.
This paper presents a roll angle calibration method for phased-array radar. Based on the built-in elevation calibration methods of phased-array radar， the proposed method utilizes the approximately linear relationship between the roll angle and the target’s elevation measurement error to calculate the roll angle. Experiments confirm that the roll angle calculated by this method has nearly the same accuracy as that of the method using laser measurement instruments. With the proposed method， the angle measurement accuracy of phased-array radar can be greatly improved.
2024, 39(6):1370-1383.
DOI: 10.16337/j.1004-9037.2024.06.007
Abstract:
In modern wars， using unmanned aerial vehicle （UAV） clusters to jam the enemy network radar for track deception is an effective way of anti-enemy radar detection. However， given the complexity and uncertainty of the battlefield environment， there are station location errors due to the limited positioning accuracy of the networked radar， and jitter errors due to the influence of air flow and control system. These can cause the generated false track points to deviate from the preset positions and the expected deception effect cannot be achieved. To solve the above problems， this paper analyzes the radar station location errors and UAV jitter errors when the radar station position， UAV position and deception distance are known and the networked radar space resolution cell （SRC） is certain. The UAV cluster successfully deceives the maximum error range allowed by the jamming network radar. For a typical networked radar system， the influence rules of two kinds of errors on the track deception effect are summarized. Some suggestions are put forward to improve the track deception effect. Numerical simulation results show that the analysis and derivation results can effectively evaluate whether the networked radar can be successfully tricked under the condition of radar position error and UAV jitter error seperately.
In modern wars， using unmanned aerial vehicle （UAV） clusters to jam the enemy network radar for track deception is an effective way of anti-enemy radar detection. However， given the complexity and uncertainty of the battlefield environment， there are station location errors due to the limited positioning accuracy of the networked radar， and jitter errors due to the influence of air flow and control system. These can cause the generated false track points to deviate from the preset positions and the expected deception effect cannot be achieved. To solve the above problems， this paper analyzes the radar station location errors and UAV jitter errors when the radar station position， UAV position and deception distance are known and the networked radar space resolution cell （SRC） is certain. The UAV cluster successfully deceives the maximum error range allowed by the jamming network radar. For a typical networked radar system， the influence rules of two kinds of errors on the track deception effect are summarized. Some suggestions are put forward to improve the track deception effect. Numerical simulation results show that the analysis and derivation results can effectively evaluate whether the networked radar can be successfully tricked under the condition of radar position error and UAV jitter error seperately.
2024, 39(2):312-323.
DOI: 10.16337/j.1004-9037.2024.02.005
Abstract:
Radar imaging technology has attracted increasing attention in the field of deep space exploration due to its fast， non-destructive， and high-resolution characteristics. To address the issue of low computational efficiency in synthetic aperture radar （SAR） 3D imaging， a fast factorized back-projection （FFBP） 3D imaging algorithm suitable for slow flyby observation modes is proposed leveraging the weak gravity and rapid spin characteristics of small solar system bodies. Initially， the equivalent motion model under slow flyby mode is analyzed， extending the imaging domain from a 2D polar coordinate system to a 3D spherical coordinate system. An in-depth analysis of aperture division and image fusion issues within the 3D FFBP algorithm is conducted， deriving rules for 2D sub-aperture division and recursive image fusion methods， along with a detailed implementation process. Finally， the effectiveness of the algorithm is validated through numerical simulations and measured data. Experimental results show that the proposed imaging algorithm significantly enhances computational efficiency. Compared to the back-projection （BP） algorithm， it can achieve a speedup ratio of 30—50 times while obtaining imaging performance comparable to the classical BP algorithm.
Radar imaging technology has attracted increasing attention in the field of deep space exploration due to its fast， non-destructive， and high-resolution characteristics. To address the issue of low computational efficiency in synthetic aperture radar （SAR） 3D imaging， a fast factorized back-projection （FFBP） 3D imaging algorithm suitable for slow flyby observation modes is proposed leveraging the weak gravity and rapid spin characteristics of small solar system bodies. Initially， the equivalent motion model under slow flyby mode is analyzed， extending the imaging domain from a 2D polar coordinate system to a 3D spherical coordinate system. An in-depth analysis of aperture division and image fusion issues within the 3D FFBP algorithm is conducted， deriving rules for 2D sub-aperture division and recursive image fusion methods， along with a detailed implementation process. Finally， the effectiveness of the algorithm is validated through numerical simulations and measured data. Experimental results show that the proposed imaging algorithm significantly enhances computational efficiency. Compared to the back-projection （BP） algorithm， it can achieve a speedup ratio of 30—50 times while obtaining imaging performance comparable to the classical BP algorithm.
2024, 39(1):120-131.
DOI: 10.16337/j.1004-9037.2024.01.011
Abstract:
An multi-scale synthetic aperture radar （SAR） image detection algorithm for ships based on improved YOLOv5 is proposed to address the large pixel scale difference of ship targets in complex scenes and missed detection caused by dense array of ships. For the neck network of YOLOv5， a bi-directional feature pyramid network （BiFPN） is adopted to enhance the multi-scale feature fusion ability of the network， and an enhanced channel-MLP （EC-MLP） module is constructed based on depthwise separable convolution （DSC） and channel MLP in its bottom-up feature fusion branch to enrich semantic information and provide more sufficient ship target context features. The global attention mechanism （GAM） is introduced to enable the network to extract input features selectively and reduce information reduction. In addition， the SIoU loss function is used to further improve the training convergence speed and detection accuracy of the network. Comparative experiments with eight other methods （Faster R-CNN， Libra R-CNN， FCOS， YOLOv5s， PP-YOLOv2， YOLOX-s， PP-YOLOE-s and YOLOv7-tiny） are conducted on SSDD and HRSID datasets. The experimental results show that the AP50 of the improved algorithm reaches 96.7% on SSDD and 95.6% on HRSID， which is superior to the comparison methods.
An multi-scale synthetic aperture radar （SAR） image detection algorithm for ships based on improved YOLOv5 is proposed to address the large pixel scale difference of ship targets in complex scenes and missed detection caused by dense array of ships. For the neck network of YOLOv5， a bi-directional feature pyramid network （BiFPN） is adopted to enhance the multi-scale feature fusion ability of the network， and an enhanced channel-MLP （EC-MLP） module is constructed based on depthwise separable convolution （DSC） and channel MLP in its bottom-up feature fusion branch to enrich semantic information and provide more sufficient ship target context features. The global attention mechanism （GAM） is introduced to enable the network to extract input features selectively and reduce information reduction. In addition， the SIoU loss function is used to further improve the training convergence speed and detection accuracy of the network. Comparative experiments with eight other methods （Faster R-CNN， Libra R-CNN， FCOS， YOLOv5s， PP-YOLOv2， YOLOX-s， PP-YOLOE-s and YOLOv7-tiny） are conducted on SSDD and HRSID datasets. The experimental results show that the AP50 of the improved algorithm reaches 96.7% on SSDD and 95.6% on HRSID， which is superior to the comparison methods.
2024, 39(4):1009-1019.
DOI: 10.16337/j.1004-9037.2024.04.019
Abstract:
The physical health status of civil aviation personnel is an important factor affecting aviation safety， among which respiration and heart rate are extremely important indicators of health. To address the limitations and interference of contact or wearable measurement systems on personnel during working， linear frequency-modulated continuous wave （FMCW） radar can be used to achieve non-contact measurement. Since vital sign signals have the characteristics of time-varying and non-stationary， to solve the problem of mode aliasing in empirical mode decomposition （EMD） in signal decomposition， the time-varying filtering based on EMD （TVF-EMD） can adaptively adjust the local cutoff frequency of the signal， effectively improving the signal separation performance and solving the mode aliasing problem. By using the intrinsic mode functions （IMF） components decomposed by TVF-EMD to reconstruct the time-domain signal corresponding to the heartbeat， the frequency and inter-beat interval （IBI） of the heartbeat signal can be estimated， and further the relevant indicators of heart rate variability （HRV） can be estimated. Simulation experiments and actual measured data processing results show that TVF-EMD can effectively separate respiration and heartbeat signals from millimeter wave radar measurement signals. At the same time， a simulation analysis of the decomposition effects of TVF-EMD and EMD methods from the aspects of mode aliasing degree and signal separation performance has been conducted， and the results show that TVF-EMD can effectively solve the mode aliasing problem. Therefore， the TVF-EMD method can accurately and effectively extract vital sign information from millimeter wave radar measurement signals， provide accurate time-domain information for IBI estimation and HRV analysis， and has a broad application prospect.
The physical health status of civil aviation personnel is an important factor affecting aviation safety， among which respiration and heart rate are extremely important indicators of health. To address the limitations and interference of contact or wearable measurement systems on personnel during working， linear frequency-modulated continuous wave （FMCW） radar can be used to achieve non-contact measurement. Since vital sign signals have the characteristics of time-varying and non-stationary， to solve the problem of mode aliasing in empirical mode decomposition （EMD） in signal decomposition， the time-varying filtering based on EMD （TVF-EMD） can adaptively adjust the local cutoff frequency of the signal， effectively improving the signal separation performance and solving the mode aliasing problem. By using the intrinsic mode functions （IMF） components decomposed by TVF-EMD to reconstruct the time-domain signal corresponding to the heartbeat， the frequency and inter-beat interval （IBI） of the heartbeat signal can be estimated， and further the relevant indicators of heart rate variability （HRV） can be estimated. Simulation experiments and actual measured data processing results show that TVF-EMD can effectively separate respiration and heartbeat signals from millimeter wave radar measurement signals. At the same time， a simulation analysis of the decomposition effects of TVF-EMD and EMD methods from the aspects of mode aliasing degree and signal separation performance has been conducted， and the results show that TVF-EMD can effectively solve the mode aliasing problem. Therefore， the TVF-EMD method can accurately and effectively extract vital sign information from millimeter wave radar measurement signals， provide accurate time-domain information for IBI estimation and HRV analysis， and has a broad application prospect.
2024, 39(1):15-30.
DOI: 10.16337/j.1004-9037.2024.01.003
Abstract:
To address the issue of resource allocation for multiple electronic jamming unmanned aerial vehicles （UAVs） against multiple multifunctional radars in the low-altitude intelligent network cooperative cognitive jamming decision-making process， a cognitive jamming decision-making approach based on digital twinning and deep reinforcement learning is proposed. Firstly， a cognitive jamming decision-making system model is established by treating the cooperative electronic jamming problem as a Markov decision process. Considering the constraints related to jamming target， jamming power， and jamming pattern selection comprehensively， the agents’ action space， state space， and reward function are constructed. Secondly， an adaptive learning rate proximal policy optimization （APPO） algorithm is proposed based on the proximal policy optimization （PPO） algorithm. Additionally， to enhance the training speed of the deep reinforcement learning algorithm in a high-fidelity manner， a digital twin-based cooperative electronic jamming decision-making model training method is presented. Simulation results demonstrate that compared with existing deep reinforcement learning algorithms， the interference efficiency of the APPO algorithm is improved by more than 30%， and the proposed training method increases the model training speed by more than 50%.
To address the issue of resource allocation for multiple electronic jamming unmanned aerial vehicles （UAVs） against multiple multifunctional radars in the low-altitude intelligent network cooperative cognitive jamming decision-making process， a cognitive jamming decision-making approach based on digital twinning and deep reinforcement learning is proposed. Firstly， a cognitive jamming decision-making system model is established by treating the cooperative electronic jamming problem as a Markov decision process. Considering the constraints related to jamming target， jamming power， and jamming pattern selection comprehensively， the agents’ action space， state space， and reward function are constructed. Secondly， an adaptive learning rate proximal policy optimization （APPO） algorithm is proposed based on the proximal policy optimization （PPO） algorithm. Additionally， to enhance the training speed of the deep reinforcement learning algorithm in a high-fidelity manner， a digital twin-based cooperative electronic jamming decision-making model training method is presented. Simulation results demonstrate that compared with existing deep reinforcement learning algorithms， the interference efficiency of the APPO algorithm is improved by more than 30%， and the proposed training method increases the model training speed by more than 50%.
2024, 39(2):471-480.
DOI: 10.16337/j.1004-9037.2024.02.019
Abstract:
Frequency estimation of sine wave signals is a common problem in the radar field. When the true frequency approaches the quantization frequency points， the calculation of the frequency shift factor in the I-Rife algorithm can introduce significant errors. In order to improve the accuracy of frequency estimation， this paper analyzes the performance and error sources of the Rife and I-Rife algorithms. By utilizing a spectral refinement method， a modified I-Rife algorithm is proposed. It replaces the amplitude of the spectral peak point with the amplitudes at 0.5 points to the left and right of the peak point， and interpolates the amplitude using the second highest frequency point. This approach allows for a more accurate estimation of the frequency offset. The proposed algorithm effectively enhances the estimation accuracy of frequency while maintaining a similar computational complexity to the original I-Rife algorithm. Simulation results demonstrate that the improved I-Rife algorithm outperforms the original I-Rife algorithm in overall performance and achieves an estimated root mean square error closer to the Cramér-Rao lower bound.
Frequency estimation of sine wave signals is a common problem in the radar field. When the true frequency approaches the quantization frequency points， the calculation of the frequency shift factor in the I-Rife algorithm can introduce significant errors. In order to improve the accuracy of frequency estimation， this paper analyzes the performance and error sources of the Rife and I-Rife algorithms. By utilizing a spectral refinement method， a modified I-Rife algorithm is proposed. It replaces the amplitude of the spectral peak point with the amplitudes at 0.5 points to the left and right of the peak point， and interpolates the amplitude using the second highest frequency point. This approach allows for a more accurate estimation of the frequency offset. The proposed algorithm effectively enhances the estimation accuracy of frequency while maintaining a similar computational complexity to the original I-Rife algorithm. Simulation results demonstrate that the improved I-Rife algorithm outperforms the original I-Rife algorithm in overall performance and achieves an estimated root mean square error closer to the Cramér-Rao lower bound.
2024, 39(1):140-153.
DOI: 10.16337/j.1004-9037.2024.01.013
Abstract:
This paper combines simultaneous transmitting and reflecting reconfigurable intelligent surfaces （STAR-RIS） with integrated sensing and communication （ISAC） systems to achieve full space communication and awareness. At the same time， a low-cost sensor is applied to STAR-RIS to achieve target sensing on STAR-RIS， solving the serious path loss problem of radar sensing. Based on this， this article researches ISAC system of STAR-RIS assisted multi user multi input single output （MU-MISO） located on both sides of STAR-RIS and a target located on the transmission side of STAR-RIS， aiming to jointly design the active beamforming at the ISAC base station and the passive beamforming matrix of STAR-RIS， in order to maximize communication sum-rate， At the same time， it meets the minimum signal to noise ratio requirements for target perception performance. To solve the non-convex problem in the optimization process， this paper proposes a block coordinate ascending algorithm based on fractional programming， which divides the optimization variables into several block variables for alternate optimization. In the subsequent active and passive beamforming problems of iterative optimization， efficient algorithms such as continuous convex approximation and semi definite relaxation are applied. Simulation results validate the advantages of deploying STAR-RIS in ISAC systems compared to traditional reconfigurable intelligent surfaces. At the same time， the proposed fractional programming-based algorithm is compared with the weighted minimum mean square error algorithm. The simulation results verify the advantages and effectiveness of the proposed algorithm in improving the sum-rate of communication.
This paper combines simultaneous transmitting and reflecting reconfigurable intelligent surfaces （STAR-RIS） with integrated sensing and communication （ISAC） systems to achieve full space communication and awareness. At the same time， a low-cost sensor is applied to STAR-RIS to achieve target sensing on STAR-RIS， solving the serious path loss problem of radar sensing. Based on this， this article researches ISAC system of STAR-RIS assisted multi user multi input single output （MU-MISO） located on both sides of STAR-RIS and a target located on the transmission side of STAR-RIS， aiming to jointly design the active beamforming at the ISAC base station and the passive beamforming matrix of STAR-RIS， in order to maximize communication sum-rate， At the same time， it meets the minimum signal to noise ratio requirements for target perception performance. To solve the non-convex problem in the optimization process， this paper proposes a block coordinate ascending algorithm based on fractional programming， which divides the optimization variables into several block variables for alternate optimization. In the subsequent active and passive beamforming problems of iterative optimization， efficient algorithms such as continuous convex approximation and semi definite relaxation are applied. Simulation results validate the advantages of deploying STAR-RIS in ISAC systems compared to traditional reconfigurable intelligent surfaces. At the same time， the proposed fractional programming-based algorithm is compared with the weighted minimum mean square error algorithm. The simulation results verify the advantages and effectiveness of the proposed algorithm in improving the sum-rate of communication.
2024, 39(5):1297-1308.
DOI: 10.16337/j.1004-9037.2024.05.020
Abstract:
In multi-user downlink indoor visible light communication system based on non-orthogonal multiple access technology （VLC-NOMA）， an iterative power allocation scheme based on weighted sum-rate maximization is proposed to solve the problem of the conflict between sum-rate and user fairness. The objective of this scheme is to maximize the weighted sum-rate， and the user fairness can be adjusted by changing the weighted factor. Since the target problem is a non-convex optimization problem， this non-convex problem is transformed into a concave problem by auxiliary variable method and convex optimization theory， then solved by the Lagrange dual method， and an iterative power allocation algorithm is designed according to the solution of the problem. The convergence of the proposed algorithm， system sum-rate and user fairness are simulated. Results show that the proposed iterative power allocation algorithm has good convergence， and VLC-NOMA system can obtain better sum-rate performance than VLC-OMA system. By adjusting the weighted factor， better system sum-rate and user fairness can be obtained than the existing power allocation scheme at the smaller expense of system sum-rate.
In multi-user downlink indoor visible light communication system based on non-orthogonal multiple access technology （VLC-NOMA）， an iterative power allocation scheme based on weighted sum-rate maximization is proposed to solve the problem of the conflict between sum-rate and user fairness. The objective of this scheme is to maximize the weighted sum-rate， and the user fairness can be adjusted by changing the weighted factor. Since the target problem is a non-convex optimization problem， this non-convex problem is transformed into a concave problem by auxiliary variable method and convex optimization theory， then solved by the Lagrange dual method， and an iterative power allocation algorithm is designed according to the solution of the problem. The convergence of the proposed algorithm， system sum-rate and user fairness are simulated. Results show that the proposed iterative power allocation algorithm has good convergence， and VLC-NOMA system can obtain better sum-rate performance than VLC-OMA system. By adjusting the weighted factor， better system sum-rate and user fairness can be obtained than the existing power allocation scheme at the smaller expense of system sum-rate.
2024, 39(2):445-455.
DOI: 10.16337/j.1004-9037.2024.02.017
Abstract:
The weighted fractional Fourier transform （WFRFT） technology can greatly change the characteristics of the signal and diversify the statistical characteristics of the signal. Thus the security of communication information is ensured. In order to solve the problem of insufficient anti-scanning ability of single-parameter WFRFT communication， taking single-parameter WFRFT as an entry point， the formation mechanism of single-parameter fractional domain is deeply studied， and its potential microscopic features and dark features are analyzed. So an implicit WFRFT communication method of jump vector （IWVJ） is proposed. Using the relationship between the modulation order and the constellation diagram， the hopping matrix and the hopping vector are established. And the control rules are formulated. In addition， the dynamic modulation order is obtained through the hopping vector control to achieve safe communication. Simulation results show that the IWVJ method has higher inverse transform demodulation similarity and lower bit error rate for licensed receivers， which is better than unlicensed receivers with universal scanning capability. At the same time， the appropriate suggestions for the setting of the demodulation order error， the basic modulation order and the jump frequency are given， so that the IWVJ method can be better applied to communication systems， and provide security information with anti-jamming， anti-interception and anti-spoofing capabilities.
The weighted fractional Fourier transform （WFRFT） technology can greatly change the characteristics of the signal and diversify the statistical characteristics of the signal. Thus the security of communication information is ensured. In order to solve the problem of insufficient anti-scanning ability of single-parameter WFRFT communication， taking single-parameter WFRFT as an entry point， the formation mechanism of single-parameter fractional domain is deeply studied， and its potential microscopic features and dark features are analyzed. So an implicit WFRFT communication method of jump vector （IWVJ） is proposed. Using the relationship between the modulation order and the constellation diagram， the hopping matrix and the hopping vector are established. And the control rules are formulated. In addition， the dynamic modulation order is obtained through the hopping vector control to achieve safe communication. Simulation results show that the IWVJ method has higher inverse transform demodulation similarity and lower bit error rate for licensed receivers， which is better than unlicensed receivers with universal scanning capability. At the same time， the appropriate suggestions for the setting of the demodulation order error， the basic modulation order and the jump frequency are given， so that the IWVJ method can be better applied to communication systems， and provide security information with anti-jamming， anti-interception and anti-spoofing capabilities.
2024, 39(5):1271-1286.
DOI: 10.16337/j.1004-9037.2024.05.018
Abstract:
Aiming at the dynamic interference problem in UAV data collection， this paper proposes a real-time optimization scheme for UAV flight trajectory. In the case of limited collection distance， by optimizing the UAV flight trajectory， the energy consumption of the UAV in the limited mission time is minimized. In order to avoid interference， the scheme is divided into two stages： initial trajectory planning and online trajectory optimization. In the initial trajectory planning stage， offline planning is carried out according to trajectory cost and corner energy consumption without considering interference； in the online trajectory optimization stage， on the basis of the initial trajectory， dynamic interference is considered， and an interference localization algorithm based on Markov prediction model is designed and the interference potential field is also proposed to optimize the initial trajectory. Simulation analysis shows that the proposed scheme can effectively improve the anti-interference performance of UAV communication and improve the UAV data collection ability.
Aiming at the dynamic interference problem in UAV data collection， this paper proposes a real-time optimization scheme for UAV flight trajectory. In the case of limited collection distance， by optimizing the UAV flight trajectory， the energy consumption of the UAV in the limited mission time is minimized. In order to avoid interference， the scheme is divided into two stages： initial trajectory planning and online trajectory optimization. In the initial trajectory planning stage， offline planning is carried out according to trajectory cost and corner energy consumption without considering interference； in the online trajectory optimization stage， on the basis of the initial trajectory， dynamic interference is considered， and an interference localization algorithm based on Markov prediction model is designed and the interference potential field is also proposed to optimize the initial trajectory. Simulation analysis shows that the proposed scheme can effectively improve the anti-interference performance of UAV communication and improve the UAV data collection ability.
2024, 39(1):44-59.
DOI: 10.16337/j.1004-9037.2024.01.005
Abstract:
As an important component of low-altitude intelligent networking， unmanned aerial vehicles （UAVs） have been widely used in the field of wireless communications. Nevertheless， the existing solutions often encounter numerous challenges when dealing with the continuously evolving scale and topology of UAV networks， such as slow convergence speed， insufficient real-time response capability， high training costs， and limited generalization abilities. To address these issues， this paper proposes an observation representation and decision-making scheme based on graph neural networks （GNNs） for UAV networks. The study initially models the relationships between UAVs and their observational entities using graph modeling techniques， designs a GNN-based representation scheme， and utilizes machine learning algorithms for pre-training to adapt to the dynamically changing observation space. For the dynamic characteristics of the decision space， the paper further introduces an edge-decision-based GNN model， which enhances adaptability to the dynamic decision space through graph modeling and edge weight fitting. Moreover， through the study of two UAV network cases， the effectiveness and superiority of the proposed scheme are validated， demonstrating its potential in practical UAV network applications.
As an important component of low-altitude intelligent networking， unmanned aerial vehicles （UAVs） have been widely used in the field of wireless communications. Nevertheless， the existing solutions often encounter numerous challenges when dealing with the continuously evolving scale and topology of UAV networks， such as slow convergence speed， insufficient real-time response capability， high training costs， and limited generalization abilities. To address these issues， this paper proposes an observation representation and decision-making scheme based on graph neural networks （GNNs） for UAV networks. The study initially models the relationships between UAVs and their observational entities using graph modeling techniques， designs a GNN-based representation scheme， and utilizes machine learning algorithms for pre-training to adapt to the dynamically changing observation space. For the dynamic characteristics of the decision space， the paper further introduces an edge-decision-based GNN model， which enhances adaptability to the dynamic decision space through graph modeling and edge weight fitting. Moreover， through the study of two UAV network cases， the effectiveness and superiority of the proposed scheme are validated， demonstrating its potential in practical UAV network applications.
2024, 39(6):1432-1444.
DOI: 10.16337/j.1004-9037.2024.06.012
Abstract:
Data compression technology can reduce the offloading energy consumption of users in mobile edge computing （MEC） by compressing computing tasks. Aiming at the problem that the communication link between the mobile users and the base station is blocked， which has an impact on communication quality， this paper proposes a task offloading scheme based on data compression to meet the requirements of emergency communication and energy-saving offloading in MEC assisted by the unmanned aerial vehicle （UAV） equipped with relay devices and edge servers. Considering constraints such as task compression ratios， system resource and the onboard energy of UAV， we formulate a problem to minimize the sum energy consumption of users. The non-convex optimization problem is modeled as a Markov decision process and the soft actor-critic algorithm based deep reinforcement learning is used to tackle the problem. The simulation results reveal that the proposed scheme achieves better convergence performance and the total energy consumption of users can be reduced by 24.7%—42.2%， compared with the benchmark algorithms.
Data compression technology can reduce the offloading energy consumption of users in mobile edge computing （MEC） by compressing computing tasks. Aiming at the problem that the communication link between the mobile users and the base station is blocked， which has an impact on communication quality， this paper proposes a task offloading scheme based on data compression to meet the requirements of emergency communication and energy-saving offloading in MEC assisted by the unmanned aerial vehicle （UAV） equipped with relay devices and edge servers. Considering constraints such as task compression ratios， system resource and the onboard energy of UAV， we formulate a problem to minimize the sum energy consumption of users. The non-convex optimization problem is modeled as a Markov decision process and the soft actor-critic algorithm based deep reinforcement learning is used to tackle the problem. The simulation results reveal that the proposed scheme achieves better convergence performance and the total energy consumption of users can be reduced by 24.7%—42.2%， compared with the benchmark algorithms.
2024, 39(2):433-444.
DOI: 10.16337/j.1004-9037.2024.02.016
Abstract:
The residual hardware impairments（HWIs） caused by the non-ideal characteristics of the transceiver hardware is unavoidable in the intelligent reflecting surface （IRS） assisted massive multiple-input multiple-output （MIMO） system， which seriously affects the uplink achievable rate. To solve this problem， a passive beamforming scheme based on the adaptive quantum bacterial foraging optimization （AQBFO） algorithm is proposed to suppress the negative impact of HWIs on the system performance. Firstly， an approximate analytical expression of the uplink achievable rate is derived based on statistical channel state information （CSI）. Then，the passive beamforming optimization scheme based on AQBFO algorithm is carried out to maximize the sum rate. Simulation results show that in IRS-assisted massive MIMO system， the passive beamforming scheme based on AQBFO algorithm can effectively suppress the influence of residual HWIS and significantly improve the uplink ergodic sum rate.
The residual hardware impairments（HWIs） caused by the non-ideal characteristics of the transceiver hardware is unavoidable in the intelligent reflecting surface （IRS） assisted massive multiple-input multiple-output （MIMO） system， which seriously affects the uplink achievable rate. To solve this problem， a passive beamforming scheme based on the adaptive quantum bacterial foraging optimization （AQBFO） algorithm is proposed to suppress the negative impact of HWIs on the system performance. Firstly， an approximate analytical expression of the uplink achievable rate is derived based on statistical channel state information （CSI）. Then，the passive beamforming optimization scheme based on AQBFO algorithm is carried out to maximize the sum rate. Simulation results show that in IRS-assisted massive MIMO system， the passive beamforming scheme based on AQBFO algorithm can effectively suppress the influence of residual HWIS and significantly improve the uplink ergodic sum rate.
2024, 39(4):898-907.
DOI: 10.16337/j.1004-9037.2024.04.001
Abstract:
In this paper， a method based on frequency group coding signal is proposed. Based on the linear frequency modulation （LFM） signal， the frequency group coding signal is constructed， which makes the pulse carrier frequency sequence of the transmitting signal a certain randomly. The anti-interference ability of the waveform is guaranteed. At the same time， a coherent processing method for the corresponding encoded signal is designed to address the issues of main lode broadening and side lode lifting problems caused by non coherent phase of frequency agile signals. Firstly， high-resolution distance compensation is applied to the compressed signal of the echo pulse， and then intra pulse coherence processing is achieved through velocity interpolation traversal and distance consistency correction. Finally， the advantages of the coding signal carrier frequency sequence are used to realize the intergroup coherent accumulation of the pulse group. In the simulation experiment， the coding signal of the building is verified compared with the advantages of LFM signal in the anti-interference， and the validity of the proposed method is compared to the sparse reconstruction algorithm based on compressive sensing.
In this paper， a method based on frequency group coding signal is proposed. Based on the linear frequency modulation （LFM） signal， the frequency group coding signal is constructed， which makes the pulse carrier frequency sequence of the transmitting signal a certain randomly. The anti-interference ability of the waveform is guaranteed. At the same time， a coherent processing method for the corresponding encoded signal is designed to address the issues of main lode broadening and side lode lifting problems caused by non coherent phase of frequency agile signals. Firstly， high-resolution distance compensation is applied to the compressed signal of the echo pulse， and then intra pulse coherence processing is achieved through velocity interpolation traversal and distance consistency correction. Finally， the advantages of the coding signal carrier frequency sequence are used to realize the intergroup coherent accumulation of the pulse group. In the simulation experiment， the coding signal of the building is verified compared with the advantages of LFM signal in the anti-interference， and the validity of the proposed method is compared to the sparse reconstruction algorithm based on compressive sensing.
2024, 39(6):1310-1325.
DOI: 10.16337/j.1004-9037.2024.06.002
Abstract:
With regard to signal detection problems in sea clutter background， traditional methods can not achieve optimal performance due to that sea clutter is an example of nonstationary signal and its statistical characteristics vary over time. The existing nonstationary signal processing methods mainly include two categories： methods based on statistical models and methods based on time series analysis. From a statistical point of view， the most commonly used method is modeling sea clutter by compound Gaussian（CG） distribution. From the perspective of time series analysis， there are many models to describe nonstationary signals including time-varying autoregressive （TVAR） model， generalized autoregressive conditional heteroskedasticity （GARCH） model and stochastic volatility （SV） model. We make comparisons of these methods mentioned above and evaluate if they could be applied to detection in sea clutter background. All of the methods can accurately describe part of the characteristics of a nonstationary sea clutter signal to some extent. However， there exist difficulties if we try to design easy-to-implement detectors. Further research about modeling the characteristics of nonstationary signals is needed for signal detection in sea clutter background.
With regard to signal detection problems in sea clutter background， traditional methods can not achieve optimal performance due to that sea clutter is an example of nonstationary signal and its statistical characteristics vary over time. The existing nonstationary signal processing methods mainly include two categories： methods based on statistical models and methods based on time series analysis. From a statistical point of view， the most commonly used method is modeling sea clutter by compound Gaussian（CG） distribution. From the perspective of time series analysis， there are many models to describe nonstationary signals including time-varying autoregressive （TVAR） model， generalized autoregressive conditional heteroskedasticity （GARCH） model and stochastic volatility （SV） model. We make comparisons of these methods mentioned above and evaluate if they could be applied to detection in sea clutter background. All of the methods can accurately describe part of the characteristics of a nonstationary sea clutter signal to some extent. However， there exist difficulties if we try to design easy-to-implement detectors. Further research about modeling the characteristics of nonstationary signals is needed for signal detection in sea clutter background.
2024, 39(1):2-14.
DOI: 10.16337/j.1004-9037.2024.01.002
Abstract:
Low-altitude intelligent network is an instrumental infrastructure for the outgrowth of low-altitude economy. However， the safety control of the unmanned aerial vehicles （UAVs） presented in such complex system faces multiple security challenges such as airspace security， data security， and spectrum security. To address these three issues simultaneously， a blockchain-based three-sided collaborative regulatory architecture， with the use of both “on-chain” and “off-chain” information， is proposed. The “on-chain” contains identity and registration information of UAVs， while the “off-chain” contains automatic dependent surveillance-broadcast （ADS-B） information and spectrum information. To solve the problem of cross-domain authentication， an effective signature algorithm is developed， which is based on the ADS-B information and certificateless signature. Furthermore， due to the lack of error correction mechanism in the ADS-B protocol， errors are easily incurred by channel noise and interference during the transmission of the ADS-B information. Consequently， the hash verification may fail. In order to alleviate such signature failure， a cross-layer signature algorithm based on error correction code is designed for correcting errors. The proposed blockchain-based three-sided collaborative regulatory platform has been well experimented over the Yangtze River low-altitude demonstration pilot zone and achieved great success.
Low-altitude intelligent network is an instrumental infrastructure for the outgrowth of low-altitude economy. However， the safety control of the unmanned aerial vehicles （UAVs） presented in such complex system faces multiple security challenges such as airspace security， data security， and spectrum security. To address these three issues simultaneously， a blockchain-based three-sided collaborative regulatory architecture， with the use of both “on-chain” and “off-chain” information， is proposed. The “on-chain” contains identity and registration information of UAVs， while the “off-chain” contains automatic dependent surveillance-broadcast （ADS-B） information and spectrum information. To solve the problem of cross-domain authentication， an effective signature algorithm is developed， which is based on the ADS-B information and certificateless signature. Furthermore， due to the lack of error correction mechanism in the ADS-B protocol， errors are easily incurred by channel noise and interference during the transmission of the ADS-B information. Consequently， the hash verification may fail. In order to alleviate such signature failure， a cross-layer signature algorithm based on error correction code is designed for correcting errors. The proposed blockchain-based three-sided collaborative regulatory platform has been well experimented over the Yangtze River low-altitude demonstration pilot zone and achieved great success.
2024, 39(1):31-43.
DOI: 10.16337/j.1004-9037.2024.01.004
Abstract:
Because of the small space range，slow target speed， and mixed environmental elements of low-altitude flight， the traditional latitude and longitude characterization cannot meet the requirements of low-altitude fine management in the Smartlink environment. Therefore the digital low-altitude airspace raster characterization metrics and optimal calibration problems are studied. Firstly， the quantitative characterization rules of multi-dimensional low-altitude airspace structural elements are constructed from the perspective of “point-line-plane”， the quantitative characterization method of multi-level raster in low-altitude airspace is proposed. Then， by determining the “point-line-plane” positional relationships of different airspace rasters， we propose a topological relationship metric of low-altitude airspace based on the raster intersection matrix. Finally， considering the optimization objectives of low-altitude unmanned aerial vehicle（UAV） collision index and low-altitude raster utilization index， as well as the node-raster matching constraints， spatial position constraints， and safety constraints of UAVs and UAVs/obstacles， we establish a multi-dimensional performance oriented optimal calibration model for the raster granularity of the low airspace， and evaluate the effectiveness and efficiency of the proposed method for the typical mission scenarios of the low airspace. The validity and optimization effect of the proposed method are verified and analyzed for typical urban low altitude flight scenarios. The experimental results show that the proposed method can optimally configure the digital low altitude raster granularity for any low-altitude airspace and UAV mission with acceptable UAV collision index and raster utilization index， so as to realize the safety and high efficiency of low altitude flight activities. The research results have certain theoretical value and application significance to support the fine management of digital low altitude airspace and the fusion operation of heterogeneous aircraft.
Because of the small space range，slow target speed， and mixed environmental elements of low-altitude flight， the traditional latitude and longitude characterization cannot meet the requirements of low-altitude fine management in the Smartlink environment. Therefore the digital low-altitude airspace raster characterization metrics and optimal calibration problems are studied. Firstly， the quantitative characterization rules of multi-dimensional low-altitude airspace structural elements are constructed from the perspective of “point-line-plane”， the quantitative characterization method of multi-level raster in low-altitude airspace is proposed. Then， by determining the “point-line-plane” positional relationships of different airspace rasters， we propose a topological relationship metric of low-altitude airspace based on the raster intersection matrix. Finally， considering the optimization objectives of low-altitude unmanned aerial vehicle（UAV） collision index and low-altitude raster utilization index， as well as the node-raster matching constraints， spatial position constraints， and safety constraints of UAVs and UAVs/obstacles， we establish a multi-dimensional performance oriented optimal calibration model for the raster granularity of the low airspace， and evaluate the effectiveness and efficiency of the proposed method for the typical mission scenarios of the low airspace. The validity and optimization effect of the proposed method are verified and analyzed for typical urban low altitude flight scenarios. The experimental results show that the proposed method can optimally configure the digital low altitude raster granularity for any low-altitude airspace and UAV mission with acceptable UAV collision index and raster utilization index， so as to realize the safety and high efficiency of low altitude flight activities. The research results have certain theoretical value and application significance to support the fine management of digital low altitude airspace and the fusion operation of heterogeneous aircraft.
2024, 39(1):83-94.
DOI: 10.16337/j.1004-9037.2024.01.008
Abstract:
Aiming at the problem of information freshness in unmanned aerial vehicle （UAV） assisted content distribution system， a UAV caching and trajectory optimization algorithm based on age of information （AoI） is proposed to alleviate the problem of long time unanswered user requests in hotspot areas. The problem of minimizing the average cost of accessing the requested content for all users is established by optimizing the ground user clustering， the UAV caching policy and the trajectory within the limited cache capacity and coverage of the UAV. The radius of coverage of UAVs is used as the radius of clustering， and the affinity propagation （AP） clustering algorithm is used to cluster the ground users. The UAV caching problem in this paper is transformed into the 01 backpacking problem， which is solved using the dynamic programming （DP） algorithm. UAV flight trajectories are solved by the genetic algorithm （GA）. Simulation results show that the algorithm proposed in this paper can effectively reduce the average cost for users to obtain the requested content.
Aiming at the problem of information freshness in unmanned aerial vehicle （UAV） assisted content distribution system， a UAV caching and trajectory optimization algorithm based on age of information （AoI） is proposed to alleviate the problem of long time unanswered user requests in hotspot areas. The problem of minimizing the average cost of accessing the requested content for all users is established by optimizing the ground user clustering， the UAV caching policy and the trajectory within the limited cache capacity and coverage of the UAV. The radius of coverage of UAVs is used as the radius of clustering， and the affinity propagation （AP） clustering algorithm is used to cluster the ground users. The UAV caching problem in this paper is transformed into the 01 backpacking problem， which is solved using the dynamic programming （DP） algorithm. UAV flight trajectories are solved by the genetic algorithm （GA）. Simulation results show that the algorithm proposed in this paper can effectively reduce the average cost for users to obtain the requested content.
2024, 39(1):71-82.
DOI: 10.16337/j.1004-9037.2024.01.007
Abstract:
The prosperity of the low-altitude ecology has continuously promoted the transformation of intelligent network services from the terrestrial to the low-altitude airspace. Low-altitude services and applications have become large-scale， collaborative， and intelligent. These trends have put forward extreme requirements for cross-domain collaboration capabilities， processing efficiency， and the security and trustworthiness of data and operations. Low-altitude intelligent network cluster collaboration using multi-device joint computing can improve the processing efficiency of complex and large-scale tasks in low-altitude intelligent networks. However， the existing schemes still have problems such as lack of cross-domain collaboration， deficiency in security and trustworthiness， and insufficient flexibility in centralized resource scheduling. Blockchain has the characteristics of immutability， openness， transparency， and collective maintenance， which is suitable for establishing efficient collaborative trust. This paper proposes a blockchain-enhanced trustworthy collaboration architecture for low-altitude intelligent network to provide on-chain cross-domain collaborative computing and trusted status synchronization services among heterogeneous low-altitude intelligent devices. We also design a multi-level consensus protocol to ensure the security and trustworthiness during the collaborative computation process. And we further analyze the freshness of the on-chain status， and propose an on-chain state correction algorithm and an efficient cluster-forming strategy for low-altitude intelligent nodes based on a queueing model. The simulation results show that the proposed architecture and protocol can improve the overall performance in terms of collaborative processing efficiency and network resource utilization.
The prosperity of the low-altitude ecology has continuously promoted the transformation of intelligent network services from the terrestrial to the low-altitude airspace. Low-altitude services and applications have become large-scale， collaborative， and intelligent. These trends have put forward extreme requirements for cross-domain collaboration capabilities， processing efficiency， and the security and trustworthiness of data and operations. Low-altitude intelligent network cluster collaboration using multi-device joint computing can improve the processing efficiency of complex and large-scale tasks in low-altitude intelligent networks. However， the existing schemes still have problems such as lack of cross-domain collaboration， deficiency in security and trustworthiness， and insufficient flexibility in centralized resource scheduling. Blockchain has the characteristics of immutability， openness， transparency， and collective maintenance， which is suitable for establishing efficient collaborative trust. This paper proposes a blockchain-enhanced trustworthy collaboration architecture for low-altitude intelligent network to provide on-chain cross-domain collaborative computing and trusted status synchronization services among heterogeneous low-altitude intelligent devices. We also design a multi-level consensus protocol to ensure the security and trustworthiness during the collaborative computation process. And we further analyze the freshness of the on-chain status， and propose an on-chain state correction algorithm and an efficient cluster-forming strategy for low-altitude intelligent nodes based on a queueing model. The simulation results show that the proposed architecture and protocol can improve the overall performance in terms of collaborative processing efficiency and network resource utilization.
2024, 39(1):60-70.
DOI: 10.16337/j.1004-9037.2024.01.006
Abstract:
With the development of networked and intelligent unmanned aerial vehicles （UAVs）， they have gradually become an important component of the low-altitude intelligent network （LAIN）. However， the effective management of UAV platforms in the LAIN still faces severe challenges. Based on the subtle features of UAV signals， individual identification of UAVs can be achieved， and illegal UAVs can be detected， thereby realizing the identification and management of UAVs in the LAIN. In response to the problem of complex channel environments and the inability to obtain illegal UAV signal samples in advance in the low-altitude domain， this paper proposes an open set identification method for UAVs based on differential time-frequency and multi-center OpenMax. Firstly， this paper proposes channel-independent differential time-frequency features to reduce the impact of multipath channel environments on radio frequency fingerprinting （RFF） features and uses data augmentation to improve the accuracy and robustness of the identification model. Secondly， this paper uses multi-center OpenMax to replace the neural network’s SoftMax layer for open set identification of UAVs. Finally， the loss function of the neural network is improved to increase the accuracy of open set recognition. The proposed algorithm is validated using real-world data. When the openness is 0.087， the open set recognition accuracy reaches 93.23%， an increase of 7.61% and 13.4% compared with the benchmark algorithms. The algorithm proposed in this paper can effectively identify individual UAVs and detect illegal UAVs appearing for the first time in complex channel environments.
With the development of networked and intelligent unmanned aerial vehicles （UAVs）， they have gradually become an important component of the low-altitude intelligent network （LAIN）. However， the effective management of UAV platforms in the LAIN still faces severe challenges. Based on the subtle features of UAV signals， individual identification of UAVs can be achieved， and illegal UAVs can be detected， thereby realizing the identification and management of UAVs in the LAIN. In response to the problem of complex channel environments and the inability to obtain illegal UAV signal samples in advance in the low-altitude domain， this paper proposes an open set identification method for UAVs based on differential time-frequency and multi-center OpenMax. Firstly， this paper proposes channel-independent differential time-frequency features to reduce the impact of multipath channel environments on radio frequency fingerprinting （RFF） features and uses data augmentation to improve the accuracy and robustness of the identification model. Secondly， this paper uses multi-center OpenMax to replace the neural network’s SoftMax layer for open set identification of UAVs. Finally， the loss function of the neural network is improved to increase the accuracy of open set recognition. The proposed algorithm is validated using real-world data. When the openness is 0.087， the open set recognition accuracy reaches 93.23%， an increase of 7.61% and 13.4% compared with the benchmark algorithms. The algorithm proposed in this paper can effectively identify individual UAVs and detect illegal UAVs appearing for the first time in complex channel environments.
2024, 39(1):95-105.
DOI: 10.16337/j.1004-9037.2024.01.009
Abstract:
To address the complex handover problem in multi-layer heterogeneous integrated networks （MLHetINet）， this paper develops a staring beam and interference cancellation algorithm to get valid data for handover analysis， simplifies the beam alignment and acquisition process， expands the coverage of the air-based network， and reduces the complexity of cross-layer handover. Firstly， aiming at the relatively high-speed movement between the ground terminal and the air base station， this paper develops a dynamic staring multi-beam forming algorithm to adaptively adjust the antenna phase and weight， and generates the main lobe in the target direction and the zero-trap for the interference source to achieve airspace isolation， thus simplifying the complexity of handover analysis. Then， considering the complexity of the air-ground channel， a multi-order interference cancellation algorithm based on column norm grouping sorting is proposed to further improve the detection accuracy of the target signal and the accuracy of handover analysis. Finally， based on the staring beam and interference cancellation algorithm， an independent handover protocol is designed for handover events in the space-air-ground three-dimensional MLHetINet， which significantly reduces the consumption of network resources. Through simulation， it is verified that the user information rate in the three-dimensional MLHetINet is significantly improved compared with that in the traditional ground network and space-ground integrated network.
To address the complex handover problem in multi-layer heterogeneous integrated networks （MLHetINet）， this paper develops a staring beam and interference cancellation algorithm to get valid data for handover analysis， simplifies the beam alignment and acquisition process， expands the coverage of the air-based network， and reduces the complexity of cross-layer handover. Firstly， aiming at the relatively high-speed movement between the ground terminal and the air base station， this paper develops a dynamic staring multi-beam forming algorithm to adaptively adjust the antenna phase and weight， and generates the main lobe in the target direction and the zero-trap for the interference source to achieve airspace isolation， thus simplifying the complexity of handover analysis. Then， considering the complexity of the air-ground channel， a multi-order interference cancellation algorithm based on column norm grouping sorting is proposed to further improve the detection accuracy of the target signal and the accuracy of handover analysis. Finally， based on the staring beam and interference cancellation algorithm， an independent handover protocol is designed for handover events in the space-air-ground three-dimensional MLHetINet， which significantly reduces the consumption of network resources. Through simulation， it is verified that the user information rate in the three-dimensional MLHetINet is significantly improved compared with that in the traditional ground network and space-ground integrated network.
2024, 39(2):456-470.
DOI: 10.16337/j.1004-9037.2024.02.018
Abstract:
In the location request service， how to protect the user’s location privacy and the data privacy of the location service provider （LSP） is a challenging issue related to WiFi fingerprinting applications. Based on the K-nearest neighbors （KNN） retrieval of the ciphertext， this paper proposes a positioning privacy protection algorithm suitable for the three party， which can effectively improve the protection intensity of the privacy of LSP fingerprint information and reduce calculation overhead. The positioning algorithm is completed by a third party based on the encrypted fingerprint database and encrypted positioning request， which is completed in the state of privacy. Through the random embedding of the location information in the fingerprint， the algorithm can avoid the physical location of the reference point （RP） in the fingerprint database. The Bloom filter （BF） is further used to complete the online matching of the reference point when hiding the access point information， which achieves rough positioning in the privacy of the user， and reduces the calculation overhead with the positioning algorithm. In the data set of public datasets and laboratory data， the security， expense and positioning performance of the two algorithms have been comprehensively evaluated. Compared with similar encryption algorithms， without reducing positioning accuracy， it further enhances the protection of data privacy.
In the location request service， how to protect the user’s location privacy and the data privacy of the location service provider （LSP） is a challenging issue related to WiFi fingerprinting applications. Based on the K-nearest neighbors （KNN） retrieval of the ciphertext， this paper proposes a positioning privacy protection algorithm suitable for the three party， which can effectively improve the protection intensity of the privacy of LSP fingerprint information and reduce calculation overhead. The positioning algorithm is completed by a third party based on the encrypted fingerprint database and encrypted positioning request， which is completed in the state of privacy. Through the random embedding of the location information in the fingerprint， the algorithm can avoid the physical location of the reference point （RP） in the fingerprint database. The Bloom filter （BF） is further used to complete the online matching of the reference point when hiding the access point information， which achieves rough positioning in the privacy of the user， and reduces the calculation overhead with the positioning algorithm. In the data set of public datasets and laboratory data， the security， expense and positioning performance of the two algorithms have been comprehensively evaluated. Compared with similar encryption algorithms， without reducing positioning accuracy， it further enhances the protection of data privacy.
2024, 39(2):481-489.
DOI: 10.16337/j.1004-9037.2024.02.020
Abstract:
Since variable fractional delay （VFD） filter contains a large number of coefficients to be solved， this paper presents a study on sparse-constrained Farrow structure variable fractional delay filter. We add a L1 regularization constraint to further enhance the sparsity based on coefficient symmetry and optimize its frequency response to approximate a desired frequency response in the minimax error sense. In addition， the alternating direction method of multipliers （ADMM） algorithm is used to iteratively obtain the filter coefficients. Simulation experiments demonstrate that the proposed sparse-constrained VFD filter not only ensures high delay accuracy but also reduces the use of multipliers and adders by 47.69% and 58.60% respectively， thus lowering system computation and complexity greatly.
Since variable fractional delay （VFD） filter contains a large number of coefficients to be solved， this paper presents a study on sparse-constrained Farrow structure variable fractional delay filter. We add a L1 regularization constraint to further enhance the sparsity based on coefficient symmetry and optimize its frequency response to approximate a desired frequency response in the minimax error sense. In addition， the alternating direction method of multipliers （ADMM） algorithm is used to iteratively obtain the filter coefficients. Simulation experiments demonstrate that the proposed sparse-constrained VFD filter not only ensures high delay accuracy but also reduces the use of multipliers and adders by 47.69% and 58.60% respectively， thus lowering system computation and complexity greatly.
2024, 39(3):761-774.
DOI: 10.16337/j.1004-9037.2024.03.022
Abstract:
Aiming at the problem of high computational overhead affecting the real-time localisation when paillier's algorithm is applied to indoor fingerprint privacy protection， this paper proposes a privacy-preserving algorithm for indoor fingerprinting positioning of mobile users to achieve trajectory anonymity and effectively improve the positioning performance. Since that the number of access points （APs） and reference points （RPs） involved in localization is the main factor affecting the time overhead of the encryption， the proposed algorithm divides the trajectory localization into continuous and discontinuous location localization. The number of APs and RPs involved in encryption is reduced by using the information of the before and after requests in continuous location localization， while the number of APs and RPs involved in encryption is reduced in discontinuous location localization. In continuous position localization， the number of APs and RPs involved in the encryption operation is reduced by using the information of before and after location requests； while in discontinuous positing localization， the coarse localization of users reduces the number of APs and RPs involved in the algorithm， thus improving the location efficiency. An optional scheme based on principal component analysis （PCA） is proposed to further improve the localization efficiency. Experimental results in a real-world environment show that the proposed algorithm can control the time required for a single positioning in both continuous and discontinuous positioning within 1 s. The positioning accuracy is improved by about 20% in continuous positioning， while the privacy protection has no effect on the positioning accuracy in discontinuous positioning. The overall performance of the localization algorithm is effectively improved.
Aiming at the problem of high computational overhead affecting the real-time localisation when paillier's algorithm is applied to indoor fingerprint privacy protection， this paper proposes a privacy-preserving algorithm for indoor fingerprinting positioning of mobile users to achieve trajectory anonymity and effectively improve the positioning performance. Since that the number of access points （APs） and reference points （RPs） involved in localization is the main factor affecting the time overhead of the encryption， the proposed algorithm divides the trajectory localization into continuous and discontinuous location localization. The number of APs and RPs involved in encryption is reduced by using the information of the before and after requests in continuous location localization， while the number of APs and RPs involved in encryption is reduced in discontinuous location localization. In continuous position localization， the number of APs and RPs involved in the encryption operation is reduced by using the information of before and after location requests； while in discontinuous positing localization， the coarse localization of users reduces the number of APs and RPs involved in the algorithm， thus improving the location efficiency. An optional scheme based on principal component analysis （PCA） is proposed to further improve the localization efficiency. Experimental results in a real-world environment show that the proposed algorithm can control the time required for a single positioning in both continuous and discontinuous positioning within 1 s. The positioning accuracy is improved by about 20% in continuous positioning， while the privacy protection has no effect on the positioning accuracy in discontinuous positioning. The overall performance of the localization algorithm is effectively improved.
2024, 39(3):750-760.
DOI: 10.16337/j.1004-9037.2024.03.021
Abstract:
This paper proposes a novel indoor positioning algorithm based on time sequences fusion in Pauta criterion-correlation coefficient convolutional neural networks （P-C-CNN）. The P-C-CNN approach integrates data points from different nodes and various time sequences， leveraging the interconnectedness of temporal and spatial data to enhance the accuracy and reliability of indoor positioning. Firstly， this method utilizes the Pauta criterion-correlation coefficient （P-C） algorithm to remove outliers in angle of arrival （AOA）-received signal strength （RSS） data， improving the quality of the training data. Secondly， the algorithm randomly selects data at intervals， reducing the training time of the model and effectively simulating the uncertainty of data selection in the online positioning phase， thus reducing overfitting of the model to the training data. Furthermore， the traditional single-frame information training method is unable to stably extract information features due to the mixture of noise. The proposed algorithm randomly selects multiple frames of fixed length from the continuously collected AOA-RSS data within time sequences fusion， and then employs convolutional neural networks （CNN） for feature extraction. This approach can avoid the issue of large error fluctuations commonly encountered in single-frame signal positioning. Finally， through extensive practical testing， this paper has validated the effectiveness of the proposed method. The experimental results demonstrate that in typical indoor environments， compared to fingerprint positioning algorithms that solely rely on RSS data or AOA information， the proposed algorithm achieves an improved classification accuracy from 91.6% to 96.4%， and the positioning accuracy is improved from 1.3 m to 0.3 m. Moreover， compared to the traditional model-based AOA-RSS joint positioning， this algorithm effectively addresses the influence of interference factors such as multipath effects observed in real-world measurements. The positioning accuracy is improved from 1.1 m to 0.3 m.
This paper proposes a novel indoor positioning algorithm based on time sequences fusion in Pauta criterion-correlation coefficient convolutional neural networks （P-C-CNN）. The P-C-CNN approach integrates data points from different nodes and various time sequences， leveraging the interconnectedness of temporal and spatial data to enhance the accuracy and reliability of indoor positioning. Firstly， this method utilizes the Pauta criterion-correlation coefficient （P-C） algorithm to remove outliers in angle of arrival （AOA）-received signal strength （RSS） data， improving the quality of the training data. Secondly， the algorithm randomly selects data at intervals， reducing the training time of the model and effectively simulating the uncertainty of data selection in the online positioning phase， thus reducing overfitting of the model to the training data. Furthermore， the traditional single-frame information training method is unable to stably extract information features due to the mixture of noise. The proposed algorithm randomly selects multiple frames of fixed length from the continuously collected AOA-RSS data within time sequences fusion， and then employs convolutional neural networks （CNN） for feature extraction. This approach can avoid the issue of large error fluctuations commonly encountered in single-frame signal positioning. Finally， through extensive practical testing， this paper has validated the effectiveness of the proposed method. The experimental results demonstrate that in typical indoor environments， compared to fingerprint positioning algorithms that solely rely on RSS data or AOA information， the proposed algorithm achieves an improved classification accuracy from 91.6% to 96.4%， and the positioning accuracy is improved from 1.3 m to 0.3 m. Moreover， compared to the traditional model-based AOA-RSS joint positioning， this algorithm effectively addresses the influence of interference factors such as multipath effects observed in real-world measurements. The positioning accuracy is improved from 1.1 m to 0.3 m.
2024, 39(3):736-749.
DOI: 10.16337/j.1004-9037.2024.03.020
Abstract:
In the application environment of human activity recognition， it is still challenging to extract sufficiently reliable features from the original sensor data. The hybrid signal decomposition technology of discrete wavelet transform （DWT） and variational mode decomposition （VMD） is used to extract the salient feature vectors from the original sensor signals to identify various human activities. Using a variety of machine learning classification algorithms， such as K-nearest neighbor， random forest， LightGBM and XGBoost， the effectiveness of the proposed algorithm is tested on UCI-HAR and SCUT-NAA data sets. Experimental results show that by using the hybrid signal decomposition technology， the recognition accuracy of all classification algorithms has been improved， with the maximum classification accuracy of 98.91% for UCI-HAR dataset， which has improved by 1.79% compared to not joining the decomposition algorithm. The maximum classification accuracy of SCUT-NAA dataset reaches 95.52%， which has improved by 3.2%. In human activity recognition， through the use of DWT-VMD hybrid signal decomposition technique， more effective features can be extracted from the original signal and the recognition accuracy can be further improved， showing the certain practical value of the technique.
In the application environment of human activity recognition， it is still challenging to extract sufficiently reliable features from the original sensor data. The hybrid signal decomposition technology of discrete wavelet transform （DWT） and variational mode decomposition （VMD） is used to extract the salient feature vectors from the original sensor signals to identify various human activities. Using a variety of machine learning classification algorithms， such as K-nearest neighbor， random forest， LightGBM and XGBoost， the effectiveness of the proposed algorithm is tested on UCI-HAR and SCUT-NAA data sets. Experimental results show that by using the hybrid signal decomposition technology， the recognition accuracy of all classification algorithms has been improved， with the maximum classification accuracy of 98.91% for UCI-HAR dataset， which has improved by 1.79% compared to not joining the decomposition algorithm. The maximum classification accuracy of SCUT-NAA dataset reaches 95.52%， which has improved by 3.2%. In human activity recognition， through the use of DWT-VMD hybrid signal decomposition technique， more effective features can be extracted from the original signal and the recognition accuracy can be further improved， showing the certain practical value of the technique.
2024, 39(4):886-897.
DOI: 10.16337/j.1004-9037.2024.04.009
Abstract:
Antenna pattern measurement is an important part of antenna measurement. Addressing the difficulty of outdoor measurement of antenna pattern， this paper presents an outdoor measurement scheme for antenna three-dimensional pattern based on the generative adversarial network. An unmanned aerial vehicle （UAV） is used to collect the antenna pattern data， modify the collected data， and obtain the direct path data when the receiving antennas match with the polarization of the antenna to be measured. Finally， the three-dimensional pattern of the antenna is reconstructed by using the trained generative adversarial network. Simulation results show that the proposed scheme can complete the measurement of antenna three-dimensional pattern efficiently and accurately， showing practical value.
Antenna pattern measurement is an important part of antenna measurement. Addressing the difficulty of outdoor measurement of antenna pattern， this paper presents an outdoor measurement scheme for antenna three-dimensional pattern based on the generative adversarial network. An unmanned aerial vehicle （UAV） is used to collect the antenna pattern data， modify the collected data， and obtain the direct path data when the receiving antennas match with the polarization of the antenna to be measured. Finally， the three-dimensional pattern of the antenna is reconstructed by using the trained generative adversarial network. Simulation results show that the proposed scheme can complete the measurement of antenna three-dimensional pattern efficiently and accurately， showing practical value.
2024, 39(5):1251-1259.
DOI: 10.16337/j.1004-9037.2024.05.016
Abstract:
To improve spectrum efficiency， this paper proposes a robust multicast transmission algorithm for intelligent reflecting surface （IRS） aided cognitive satellite and terrestrial network （CSTN）. Specifically， the satellite uses multicast technology to serve multiple primary users， while the terrestrial base station （BS）， sharing spectrum resources with the satellite network， serves direct users and blocked users through space division multiple access technique and intelligent reflecting surfaces， respectively. Then， a joint optimization problem is formulated to minimize the BS transmit power， while satisfying the outage constraints of both the signal-to-interference-plus-noise ratio of terrestrial users and the interference power of the primary users. To address this nonconvex problem， the nonconvex outage constraint is first transformed into a deterministic form with the assistance of the cumulative distribution function of the exponential distribution. Then， a robust beamforming algorithm combining alternating optimization with semi-positive definite relaxation is proposed to obtain a solution with better performance. Computer simulation results demonstrate the robustness and superiority of the proposed algorithm.
To improve spectrum efficiency， this paper proposes a robust multicast transmission algorithm for intelligent reflecting surface （IRS） aided cognitive satellite and terrestrial network （CSTN）. Specifically， the satellite uses multicast technology to serve multiple primary users， while the terrestrial base station （BS）， sharing spectrum resources with the satellite network， serves direct users and blocked users through space division multiple access technique and intelligent reflecting surfaces， respectively. Then， a joint optimization problem is formulated to minimize the BS transmit power， while satisfying the outage constraints of both the signal-to-interference-plus-noise ratio of terrestrial users and the interference power of the primary users. To address this nonconvex problem， the nonconvex outage constraint is first transformed into a deterministic form with the assistance of the cumulative distribution function of the exponential distribution. Then， a robust beamforming algorithm combining alternating optimization with semi-positive definite relaxation is proposed to obtain a solution with better performance. Computer simulation results demonstrate the robustness and superiority of the proposed algorithm.
2024, 39(5):1287-1296.
DOI: 10.16337/j.1004-9037.2024.05.019
Abstract:
To address the uplink transmit power minimization problem for multi-cell scenarios， this paper proposes an uplink transmission scheme for the coordinated multiple point-nonorthogonal multiple access （CoMP-NOMA） system with the collaboration of multiple intelligent reflecting surfaces （IRSs）. Specifically， a couple of IRSs are deployed at the cell-center and the cell-edge respectively， to improve the transmission quality for both the cell-center and the cell-edge users， in which the inter-IRS reflection between the cell-center and the cell-edge IRSs is considered. To solve the formulated power minimization problem， the relation between the power allocation coefficients and the phase shifts is developed. Further， the joint optimization problem of power allocation and phase shift is converted into a pure phase shift determination problem， which is transformed to a series of one-dimensional search problems by using the sequential rotation method. Simulation results demonstrate that the proposed solution significantly outperforms other benchmark schemes in terms of transmit power consumption under the same simulation setups.
To address the uplink transmit power minimization problem for multi-cell scenarios， this paper proposes an uplink transmission scheme for the coordinated multiple point-nonorthogonal multiple access （CoMP-NOMA） system with the collaboration of multiple intelligent reflecting surfaces （IRSs）. Specifically， a couple of IRSs are deployed at the cell-center and the cell-edge respectively， to improve the transmission quality for both the cell-center and the cell-edge users， in which the inter-IRS reflection between the cell-center and the cell-edge IRSs is considered. To solve the formulated power minimization problem， the relation between the power allocation coefficients and the phase shifts is developed. Further， the joint optimization problem of power allocation and phase shift is converted into a pure phase shift determination problem， which is transformed to a series of one-dimensional search problems by using the sequential rotation method. Simulation results demonstrate that the proposed solution significantly outperforms other benchmark schemes in terms of transmit power consumption under the same simulation setups.
2024, 39(6):1410-1419.
DOI: 10.16337/j.1004-9037.2024.06.010
Abstract:
Modulation recognition technology has been widely used in cognitive radio and electronic reconnaissance countermeasures. In recent years， thanks to the powerful feature extraction ability of deep neural networks， the research of automatic modulation recognition based on deep learning has made great progress. In practical modulation recognition scenarios， modulation signals usually transmit bit sequences without semantic information， and each modulation symbol appears in waveforms with uniform probability， so its feature information is uniformly distributed in signal. However， existing automatic modulation recognition methods based on deep learning usually use structures of convolutional neural network （CNN） or recurrent neural network （RNN）. They are difficult to be adapted to the data distribution in the scenarios above and thus fail to make full use of the global characteristics of long sequential data. Therefore， the accuracy of modulation recognition can be further improved by exploiting the sequential information. In this paper， an automatic modulation recognition method based on improved Transformer， AMR-former， is proposed. Firstly， the input signal is preprocessed to strengthen the temporal characteristics. Then， the AMR-Encoder structure for feature extraction is designed and implemented by combining the multi-head attention mechanism and long short-term memory （LSTM） network， which effectively improves the ability of global temporal feature extraction and provides richer representations for the subsequent recognition and classification. Experiments on the RadioML 2016.10a dataset show that the average recognition accuracy of the AMR-former method reaches 91.90% with the signal-to-noise ratio （SNR） from 0 dB to18 dB， which is 6.38%，2.15%，1.99% and 1.75% higher than the typical networks of GRU， PET-CGDNN， LSTM and MCLDNN， respectively.
Modulation recognition technology has been widely used in cognitive radio and electronic reconnaissance countermeasures. In recent years， thanks to the powerful feature extraction ability of deep neural networks， the research of automatic modulation recognition based on deep learning has made great progress. In practical modulation recognition scenarios， modulation signals usually transmit bit sequences without semantic information， and each modulation symbol appears in waveforms with uniform probability， so its feature information is uniformly distributed in signal. However， existing automatic modulation recognition methods based on deep learning usually use structures of convolutional neural network （CNN） or recurrent neural network （RNN）. They are difficult to be adapted to the data distribution in the scenarios above and thus fail to make full use of the global characteristics of long sequential data. Therefore， the accuracy of modulation recognition can be further improved by exploiting the sequential information. In this paper， an automatic modulation recognition method based on improved Transformer， AMR-former， is proposed. Firstly， the input signal is preprocessed to strengthen the temporal characteristics. Then， the AMR-Encoder structure for feature extraction is designed and implemented by combining the multi-head attention mechanism and long short-term memory （LSTM） network， which effectively improves the ability of global temporal feature extraction and provides richer representations for the subsequent recognition and classification. Experiments on the RadioML 2016.10a dataset show that the average recognition accuracy of the AMR-former method reaches 91.90% with the signal-to-noise ratio （SNR） from 0 dB to18 dB， which is 6.38%，2.15%，1.99% and 1.75% higher than the typical networks of GRU， PET-CGDNN， LSTM and MCLDNN， respectively.
2024, 39(6):1420-1431.
DOI: 10.16337/j.1004-9037.2024.06.011
Abstract:
Ordered successive interference cancellation （OSIC） is a commonly utilized signal detection algorithm in multiple input multiple output （MIMO） systems. However， the algorithm’s performance in terms of throughput and latency is constrained by the computational complexity of the channel matrix inverse operation. Therefore， matrix inverse decomposition pre-processing with low computational complexity and high speed is the key to hardware implementation of the algorithm. In this paper， we adopt a hardware-accelerated matrix pre-processing scheme for sorted orthogonal triangle （QR） decomposition of the channel matrix， in which the sorting process introducing a fast estimation method for complex-valued 1-norm to eliminate complex modulus computation. The QR decomposition process uses a deeply pipelined coordinate rotation digital computer （CORDIC） iterative method to eliminate the element vectorization and nulling rotation angle computation in the Givens rotation process， thus a pipeline circuit structure with a reusable Givens rotation structure for QR decomposition is designed， obviating the necessity for multipliers in the matrix decomposition process. Simulation results demonstrate that the OSIC enhancement algorithm proposed achieves the bit error rate（BER） performance comparable to that of the signal-to-noise ratio-based OSIC detection algorithm. The CORDIC iterative Givens rotation structure proposed in this paper can achieve highly time-sharing multiplex. It significantly improves the system parallelism and reduces the resource consumption， and the system design clock attains up to 250 MHz， and the matrix decomposition throughput reaches 1.88 M Matrices/s， meeting the processing throughput and latency requirements of 4 or more antennas MIMO systems at the receiver.
Ordered successive interference cancellation （OSIC） is a commonly utilized signal detection algorithm in multiple input multiple output （MIMO） systems. However， the algorithm’s performance in terms of throughput and latency is constrained by the computational complexity of the channel matrix inverse operation. Therefore， matrix inverse decomposition pre-processing with low computational complexity and high speed is the key to hardware implementation of the algorithm. In this paper， we adopt a hardware-accelerated matrix pre-processing scheme for sorted orthogonal triangle （QR） decomposition of the channel matrix， in which the sorting process introducing a fast estimation method for complex-valued 1-norm to eliminate complex modulus computation. The QR decomposition process uses a deeply pipelined coordinate rotation digital computer （CORDIC） iterative method to eliminate the element vectorization and nulling rotation angle computation in the Givens rotation process， thus a pipeline circuit structure with a reusable Givens rotation structure for QR decomposition is designed， obviating the necessity for multipliers in the matrix decomposition process. Simulation results demonstrate that the OSIC enhancement algorithm proposed achieves the bit error rate（BER） performance comparable to that of the signal-to-noise ratio-based OSIC detection algorithm. The CORDIC iterative Givens rotation structure proposed in this paper can achieve highly time-sharing multiplex. It significantly improves the system parallelism and reduces the resource consumption， and the system design clock attains up to 250 MHz， and the matrix decomposition throughput reaches 1.88 M Matrices/s， meeting the processing throughput and latency requirements of 4 or more antennas MIMO systems at the receiver.
2024, 39(6):1384-1398.
DOI: 10.16337/j.1004-9037.2024.06.008
Abstract:
Radio frequency （RF） tunable filtering and RF interference cancellation have always been the focus of RF interference suppression research. The combination of these two key technologies is considered to be a RF protection scheme to improve anti-known cooperative interference ability for receivers. This paper systematically sorts out the merits and drawbacks of the available RF protection circuits and summarizes the advanced technologies of RF tunable filtering and RF interference cancellation after investigation. Aiming at application requirements of anti-unknown non-cooperative interference， based on the main path of RF spectrum sensing and the auxiliary path of RF interference cancellation， the architecture of wideband adaptive RF protection （WARP） for spectrum sensing receivers is preliminarily proposed， and the difficult problems are discussed lastly.
Radio frequency （RF） tunable filtering and RF interference cancellation have always been the focus of RF interference suppression research. The combination of these two key technologies is considered to be a RF protection scheme to improve anti-known cooperative interference ability for receivers. This paper systematically sorts out the merits and drawbacks of the available RF protection circuits and summarizes the advanced technologies of RF tunable filtering and RF interference cancellation after investigation. Aiming at application requirements of anti-unknown non-cooperative interference， based on the main path of RF spectrum sensing and the auxiliary path of RF interference cancellation， the architecture of wideband adaptive RF protection （WARP） for spectrum sensing receivers is preliminarily proposed， and the difficult problems are discussed lastly.
2024, 39(6):1399-1409.
DOI: 10.16337/j.1004-9037.2024.06.009
Abstract:
In response to the problem of holes in traditional coprime planar array （CPA） structures when using difference coarray （DCA） for two-dimensional direction of arrival （DOA） estimation， this paper proposes a hole-free coprime planar array （HFCPA） structure. The array is obtained by extending hole-free coprime linear arrays along the x and y axes， and its DCA is a hole-free rectangular array. Furthermore， this paper presents the optimal HFCPA structure to maximize the available continuous degrees of freedom. Simulation results demonstrate the superiority of the proposed array structure over existing coprime planar array structures in terms of the number of continuous degrees of freedom， virtualization efficiency， and two-dimensional DOA estimation performance.
In response to the problem of holes in traditional coprime planar array （CPA） structures when using difference coarray （DCA） for two-dimensional direction of arrival （DOA） estimation， this paper proposes a hole-free coprime planar array （HFCPA） structure. The array is obtained by extending hole-free coprime linear arrays along the x and y axes， and its DCA is a hole-free rectangular array. Furthermore， this paper presents the optimal HFCPA structure to maximize the available continuous degrees of freedom. Simulation results demonstrate the superiority of the proposed array structure over existing coprime planar array structures in terms of the number of continuous degrees of freedom， virtualization efficiency， and two-dimensional DOA estimation performance.
2025, 40(6):1518-1526.
DOI: 10.16337/j.1004-9037.2025.06.011
Abstract:
Existing multi-station fusion technologies focus on utilizing intuitive features such as echo amplitude correlation and spatial location. However， the comprehensiveness of manual feature extraction is insufficient， which can easily lead to signal resource waste， incomplete feature extraction， and limited generalization of discrimination algorithms. To address this issue， this paper innovatively proposes a jamming identification strategy that integrates multi-radar cooperative detection with convolutional neural network. This approach leverages convolutional neural networks to deeply explore unknown information in echo data， extracting differences between real and false targets in multidimensional deep features， surpassing single spatial correlation differences， and achieving deception jamming identification. Finally， simulation experiments validate the feasibility of the proposed method in resisting deception jamming and analyze the effects of target size， multi-station radar deployment and phase errors on the proposed algorithm.
Existing multi-station fusion technologies focus on utilizing intuitive features such as echo amplitude correlation and spatial location. However， the comprehensiveness of manual feature extraction is insufficient， which can easily lead to signal resource waste， incomplete feature extraction， and limited generalization of discrimination algorithms. To address this issue， this paper innovatively proposes a jamming identification strategy that integrates multi-radar cooperative detection with convolutional neural network. This approach leverages convolutional neural networks to deeply explore unknown information in echo data， extracting differences between real and false targets in multidimensional deep features， surpassing single spatial correlation differences， and achieving deception jamming identification. Finally， simulation experiments validate the feasibility of the proposed method in resisting deception jamming and analyze the effects of target size， multi-station radar deployment and phase errors on the proposed algorithm.
2025, 40(1):197-206.
DOI: 10.16337/j.1004-9037.2025.01.015
Abstract:
In order to improve the accuracy of radar system parameter estimation， this paper proposes a data fusion and parameter fusion method for parameter estimation based on Bayesian principle. We derive the distance information， entropy error and mean square error （MSE） for a multi-radar system under additive complex Gaussian noise conditions， and derive an upper bound on the distance information. The theoretical derivation shows that the maximum a posteriori estimate （MAP） of the position estimation is consistent with the maximum ratio of the position information. The equivalent signal-to-noise ratio of the multi-radar system is equal to the sum of the signal-to-noise ratios of radars in the system. Experimental results indicate that， in general， the performance of data fusion is always superior to that of parameter fusion. However， data fusion relies on the assumption of uniform distribution and requires distortion-free acquisition of the received signals from all nodes， representing an idealized scenario. In contrast， parameter fusion is more aligned with real-world scenarios， and its estimation accuracy is not significantly inferior to that of data fusion. The findings of this study provide valuable guidance for improving the accuracy of target parameter estimation in practical environments.
In order to improve the accuracy of radar system parameter estimation， this paper proposes a data fusion and parameter fusion method for parameter estimation based on Bayesian principle. We derive the distance information， entropy error and mean square error （MSE） for a multi-radar system under additive complex Gaussian noise conditions， and derive an upper bound on the distance information. The theoretical derivation shows that the maximum a posteriori estimate （MAP） of the position estimation is consistent with the maximum ratio of the position information. The equivalent signal-to-noise ratio of the multi-radar system is equal to the sum of the signal-to-noise ratios of radars in the system. Experimental results indicate that， in general， the performance of data fusion is always superior to that of parameter fusion. However， data fusion relies on the assumption of uniform distribution and requires distortion-free acquisition of the received signals from all nodes， representing an idealized scenario. In contrast， parameter fusion is more aligned with real-world scenarios， and its estimation accuracy is not significantly inferior to that of data fusion. The findings of this study provide valuable guidance for improving the accuracy of target parameter estimation in practical environments.
2025, 40(6):1505-1517.
DOI: 10.16337/j.1004-9037.2025.06.010
Abstract:
Currently， LiDAR- and vision-based 3D reconstruction technologies are widely used in terrain scene measurement. Although various 3D imaging methods based on LiDAR and cameras have been developed， each has limitations. RGB-D cameras can capture both color and depth information but often have lower accuracy than LiDAR， while 3D LiDAR provides high-precision spatial data but lacks color information and is typically expensive. This paper proposes a 3D-RGB imaging method based on data-level fusion of a 2D LiDAR and multi-view cameras， integrating 3D-RGB point cloud data from a 2D LiDAR and four cameras from different viewpoints. The method achieves accurate and dense 3D-RGB imaging through 3D-RGB point enhancement， feature plane detection and extraction， and global consistency alignment. First， fusing RGB and point cloud data enhances data quality， while feature plane detection optimizes geometric structure representation. Then， a global consistency alignment strategy reduces accumulated errors and improves overall imaging accuracy. Experimental results show that compared with multi-line LiDAR solutions， the proposed method offers advantages in imaging density and accuracy， with an overall error of less than 0.15 m， demonstrating its potential for 3D reconstruction and environmental surveying in complex environments.
Currently， LiDAR- and vision-based 3D reconstruction technologies are widely used in terrain scene measurement. Although various 3D imaging methods based on LiDAR and cameras have been developed， each has limitations. RGB-D cameras can capture both color and depth information but often have lower accuracy than LiDAR， while 3D LiDAR provides high-precision spatial data but lacks color information and is typically expensive. This paper proposes a 3D-RGB imaging method based on data-level fusion of a 2D LiDAR and multi-view cameras， integrating 3D-RGB point cloud data from a 2D LiDAR and four cameras from different viewpoints. The method achieves accurate and dense 3D-RGB imaging through 3D-RGB point enhancement， feature plane detection and extraction， and global consistency alignment. First， fusing RGB and point cloud data enhances data quality， while feature plane detection optimizes geometric structure representation. Then， a global consistency alignment strategy reduces accumulated errors and improves overall imaging accuracy. Experimental results show that compared with multi-line LiDAR solutions， the proposed method offers advantages in imaging density and accuracy， with an overall error of less than 0.15 m， demonstrating its potential for 3D reconstruction and environmental surveying in complex environments.
2025, 40(1):2-26.
DOI: 10.16337/j.1004-9037.2025.01.002
Abstract:
As “low altitude economy” is included in the government reports， it become the hot topic in 2024. Due to the advantages of high efficiency， flexibility， low cost， and multi payload， unmanned aerial vehicles （UAVs） are regarded as the main form of “low altitude economy”. As the key factor to guarantee flight safety and communication security， the spectrum management of UAV communication is an indispensable factor in promoting the vigorous development of the “low altitude economy”. This paper starts with the changes of UAV spectrum management policies from 2015 to 2023， and then deeply explores the regulations， standards and technologies of UAV communication， including the operating frequency bands and flight supervision， as well as standard specifications represented by international organizations， such as International Telecommunication Union （ITU）， Institute of Electrical and Electronics Engineers （IEEE） and The 3rd Generation Partnership Project （3GPP）. The subjects of channel models and interference mitigation strategies that closely related to UAV communication spectrum management are also discussed. Finally， current challenges and future research directions of UAV communication spectrum management are presented.
As “low altitude economy” is included in the government reports， it become the hot topic in 2024. Due to the advantages of high efficiency， flexibility， low cost， and multi payload， unmanned aerial vehicles （UAVs） are regarded as the main form of “low altitude economy”. As the key factor to guarantee flight safety and communication security， the spectrum management of UAV communication is an indispensable factor in promoting the vigorous development of the “low altitude economy”. This paper starts with the changes of UAV spectrum management policies from 2015 to 2023， and then deeply explores the regulations， standards and technologies of UAV communication， including the operating frequency bands and flight supervision， as well as standard specifications represented by international organizations， such as International Telecommunication Union （ITU）， Institute of Electrical and Electronics Engineers （IEEE） and The 3rd Generation Partnership Project （3GPP）. The subjects of channel models and interference mitigation strategies that closely related to UAV communication spectrum management are also discussed. Finally， current challenges and future research directions of UAV communication spectrum management are presented.
2025, 40(6):1490-1504.
DOI: 10.16337/j.1004-9037.2025.06.009
Abstract:
With the development of 5G and 6G communications， satellite communication， which can provide the global seamless coverage capability， has become an indispensable and important role. In view of the open channel characteristics of satellite communication， the security of satellite communication has been paid more and more attention， especially the demand for satellite secure communication in national defense and military communication. Based on the architecture of the multi-satellite and terminal multi-antenna （MS-TMA） satellite communication system， from the physical layer of security and covert communication theory， a satellite covert communication strategy is put forward. Theoretical analysis and simulations validate the secure communication performance of the approach. The findings offer significant insights for advancing satellite secure communication theory and the practical applications.
With the development of 5G and 6G communications， satellite communication， which can provide the global seamless coverage capability， has become an indispensable and important role. In view of the open channel characteristics of satellite communication， the security of satellite communication has been paid more and more attention， especially the demand for satellite secure communication in national defense and military communication. Based on the architecture of the multi-satellite and terminal multi-antenna （MS-TMA） satellite communication system， from the physical layer of security and covert communication theory， a satellite covert communication strategy is put forward. Theoretical analysis and simulations validate the secure communication performance of the approach. The findings offer significant insights for advancing satellite secure communication theory and the practical applications.
95 Performance Analysis of Sensing and Communication Probability Fusion System for Rayleigh Channels
2025, 40(2):446-455.
DOI: 10.16337/j.1004-9037.2025.02.013
Abstract:
This article presents a Rayleigh fading channel model of integrated sensing and communication （ISAC）， proposes a method of probability fusion after integrated sensing and communication （PF-ISAC）， and derives the PF-ISAC channel model. It is theoretically proved that when the sensing signal to noise ratio（SNR） approaches infinity， the ISAC model degenerats into an ideal channel state information（CSI） scenario， and when the sensing SNR approaches zero， the ISAC model degenerats into a scenario where CSI is unknown. The relationship between mutual information and SNR of the PF-ISAC system is given. As the SNR increases， the channel capacity of the mutual information gradually approaches the capacity of the ideal CSI when the CSI is unknown. This article proposes a probability fusion after maximum a posterior （PF-MAP） detection method and a probability fusion after maximum likelihood （PF-ML） detection method， and compares them with the minimum mean square error（MMSE） estimation-MMSE detection method（MMSE-MMSE）. The results show that PF-MAP performs similarly to MMSE-MMSE at low to medium SNRs， while PF-MAP outperforms MMSE-MMSE at high SNRs. We evaluate the error performance of the PF-ISAC system using entropy error （EE）. Results show that MMSE-MMSE， PF-MAP， PF-ML have significant gaps from the theoretical optimal performance EE. Finally， a scheme for power allocation in ISAC system is proposed. When the total power is given， the performance of two-stage equal power allocation in ISAC system is close to optimal.
This article presents a Rayleigh fading channel model of integrated sensing and communication （ISAC）， proposes a method of probability fusion after integrated sensing and communication （PF-ISAC）， and derives the PF-ISAC channel model. It is theoretically proved that when the sensing signal to noise ratio（SNR） approaches infinity， the ISAC model degenerats into an ideal channel state information（CSI） scenario， and when the sensing SNR approaches zero， the ISAC model degenerats into a scenario where CSI is unknown. The relationship between mutual information and SNR of the PF-ISAC system is given. As the SNR increases， the channel capacity of the mutual information gradually approaches the capacity of the ideal CSI when the CSI is unknown. This article proposes a probability fusion after maximum a posterior （PF-MAP） detection method and a probability fusion after maximum likelihood （PF-ML） detection method， and compares them with the minimum mean square error（MMSE） estimation-MMSE detection method（MMSE-MMSE）. The results show that PF-MAP performs similarly to MMSE-MMSE at low to medium SNRs， while PF-MAP outperforms MMSE-MMSE at high SNRs. We evaluate the error performance of the PF-ISAC system using entropy error （EE）. Results show that MMSE-MMSE， PF-MAP， PF-ML have significant gaps from the theoretical optimal performance EE. Finally， a scheme for power allocation in ISAC system is proposed. When the total power is given， the performance of two-stage equal power allocation in ISAC system is close to optimal.
2025, 40(3):784-792.
DOI: 10.16337/j.1004-9037.2025.03.017
Abstract:
To study the performance of multi-reconfigurable intelligent surface （RIS） assisted communication network with the presence of the same frequency interference at the receiving end， this paper deploys multiple RIS of different geometric sizes as relays in the wireless channel to improve the performance of the communication network， and assumes that the wireless channels associated with different RISs are independent and non-uniformly distributed. Channels associated with different reflector surfaces in the same RIS are independently and identically distributed. The end-to-end channel coefficients are approximated to the Gamma distribution， and the exact expressions of outage probability（OP）、ergodic capacity（EC） and OP asymptotic expressions are derived based on the Gamma distribution. Monte Carlo simulation is used to verify the correctness of the analysis results. The research shows that the number of RIS， the number of interference items and the interference signal power play a crucial role in the cooperative transmission performance of multi-RIS auxiliary communication networks.
To study the performance of multi-reconfigurable intelligent surface （RIS） assisted communication network with the presence of the same frequency interference at the receiving end， this paper deploys multiple RIS of different geometric sizes as relays in the wireless channel to improve the performance of the communication network， and assumes that the wireless channels associated with different RISs are independent and non-uniformly distributed. Channels associated with different reflector surfaces in the same RIS are independently and identically distributed. The end-to-end channel coefficients are approximated to the Gamma distribution， and the exact expressions of outage probability（OP）、ergodic capacity（EC） and OP asymptotic expressions are derived based on the Gamma distribution. Monte Carlo simulation is used to verify the correctness of the analysis results. The research shows that the number of RIS， the number of interference items and the interference signal power play a crucial role in the cooperative transmission performance of multi-RIS auxiliary communication networks.
2025, 40(4):962-971.
DOI: 10.16337/j.1004-9037.2025.04.010
Abstract:
In channel estimation tasks for intelligent reflecting surface （IRS）-assisted communication systems when line-of-sight communication between user equipment and base station （BS） is blocked， this paper proposes a neural network based on noise suppression in the latent feature space， which can realize accurate channel estimation. The neural network combines the variational auto-encoder （VAE） and UNet to reduce the noise in the input signal while performing channel estimation. Firstly， the VAE model takes noise-free BS received signals as input， with the objective of minimizing the error between the estimated noise-free BS received signals and their true value， so that the encoder of the VAE model maps a feature vector as a potential representation of the pure received signal. Secondly， the VAE model part is fixed. The entire network is trained using noisy BS received signals as input to the UNet model， in which the noise-free latent feature vectors learned by the VAE assist the encoder of the UNet model in learning noise-free feature representations. Subsequently， the pure feature representations are fed into the decoder of the UNet model to achieve the channel estimation task. Finally， during the estimation phase， only the UNet model part is utilized， which effectively reduces computational complexity. The results of simulation experiments demonstrate that the proposed channel estimation method can effectively suppress noisy information in the feature space， and can estimate the channel information more accurately with lower time complexity.
In channel estimation tasks for intelligent reflecting surface （IRS）-assisted communication systems when line-of-sight communication between user equipment and base station （BS） is blocked， this paper proposes a neural network based on noise suppression in the latent feature space， which can realize accurate channel estimation. The neural network combines the variational auto-encoder （VAE） and UNet to reduce the noise in the input signal while performing channel estimation. Firstly， the VAE model takes noise-free BS received signals as input， with the objective of minimizing the error between the estimated noise-free BS received signals and their true value， so that the encoder of the VAE model maps a feature vector as a potential representation of the pure received signal. Secondly， the VAE model part is fixed. The entire network is trained using noisy BS received signals as input to the UNet model， in which the noise-free latent feature vectors learned by the VAE assist the encoder of the UNet model in learning noise-free feature representations. Subsequently， the pure feature representations are fed into the decoder of the UNet model to achieve the channel estimation task. Finally， during the estimation phase， only the UNet model part is utilized， which effectively reduces computational complexity. The results of simulation experiments demonstrate that the proposed channel estimation method can effectively suppress noisy information in the feature space， and can estimate the channel information more accurately with lower time complexity.
2025, 40(1):72-85.
DOI: 10.16337/j.1004‐9037.2025.01.006
Abstract:
Unmanned aerial vehicle （UAV）-mounted base stations possess characteristics， such as rapid deployment and flexible coverage， making them an effective solution for emergency communication in railways. However， the low-altitude communication network formed by UAVs faces the constraint of the limited energy storage and the risk of data eavesdropping or tampering. This paper introduces blockchain technology into a UAV-assisted railway wireless communication system to ensure data security. Considering the constraints on transmission delay and data queue stability， this paper proposes a joint optimization problem aimed at minimizing the energy consumption of the UAV-assisted communication system and the latency of the blockchain. To solve this non-convex， mixed-integer， and time-varying stochastic optimization problem， a Lyapunov-based drift-plus-penalty method is proposed to transform the long-term stochastic optimization problem into sub-problems of multiple time slots. Deep reinforcement learning based on D3QN-TD3 is designed to solve these sub-problems， and then the optimal association strategies and power control for each time slot are obtained. Experimental results demonstrate the significant effectiveness of the proposed method in reducing the energy consumption and delay.
Unmanned aerial vehicle （UAV）-mounted base stations possess characteristics， such as rapid deployment and flexible coverage， making them an effective solution for emergency communication in railways. However， the low-altitude communication network formed by UAVs faces the constraint of the limited energy storage and the risk of data eavesdropping or tampering. This paper introduces blockchain technology into a UAV-assisted railway wireless communication system to ensure data security. Considering the constraints on transmission delay and data queue stability， this paper proposes a joint optimization problem aimed at minimizing the energy consumption of the UAV-assisted communication system and the latency of the blockchain. To solve this non-convex， mixed-integer， and time-varying stochastic optimization problem， a Lyapunov-based drift-plus-penalty method is proposed to transform the long-term stochastic optimization problem into sub-problems of multiple time slots. Deep reinforcement learning based on D3QN-TD3 is designed to solve these sub-problems， and then the optimal association strategies and power control for each time slot are obtained. Experimental results demonstrate the significant effectiveness of the proposed method in reducing the energy consumption and delay.
2025, 40(1):147-162.
DOI: 10.16337/j.1004-9037.2025.01.011
Abstract:
Different from ground communications， space communications usually involve signal vehicles that travel over long-distance at a high speed. In these scenarios， the signal transmission faces two difficulties： A low signal-to-noise-ratio （SNR） caused by the long-distance path-loss and a dynamic Doppler-shift caused by the high-speed movement. For Doppler-shift acquisition， the low SNR requires a long-time accumulation to accumulate a large number of signals. However， during this period， the dynamic Doppler-shift disperses all signals’ total energy over a wide frequency range. To address the energy dispersion problem， this paper proposes a local-clustering-acquisition （LCA） algorithm. The LCA algorithm uses the largest elements from the global-ranges to construct a local-range， then selects some large elements from this local-range for clustering， and finally searches the largest cluster from the clustering results to obtain the acquisition result. Theoretical analysis and simulation validation results demonstrate the LCA algorithm’s significant advantages in increasing acquisition probability， as compared with the existing algorithms.
Different from ground communications， space communications usually involve signal vehicles that travel over long-distance at a high speed. In these scenarios， the signal transmission faces two difficulties： A low signal-to-noise-ratio （SNR） caused by the long-distance path-loss and a dynamic Doppler-shift caused by the high-speed movement. For Doppler-shift acquisition， the low SNR requires a long-time accumulation to accumulate a large number of signals. However， during this period， the dynamic Doppler-shift disperses all signals’ total energy over a wide frequency range. To address the energy dispersion problem， this paper proposes a local-clustering-acquisition （LCA） algorithm. The LCA algorithm uses the largest elements from the global-ranges to construct a local-range， then selects some large elements from this local-range for clustering， and finally searches the largest cluster from the clustering results to obtain the acquisition result. Theoretical analysis and simulation validation results demonstrate the LCA algorithm’s significant advantages in increasing acquisition probability， as compared with the existing algorithms.
2025, 40(5):1261-1269.
DOI: 10.16337/j.1004-9037.2025.05.012
Abstract:
Under the condition of few-shot specific communication emitter identification， the difficulty of extracting individual features of communication radiation source by the existing deep learning algorithm increases， and the recognition rate decreases. To solve this problem， this paper proposes a recognition method to construct a shallow neural network by fusing attention mechanism and broad learning. Firstly， broad learning is introduced to simplify the network model and reduce the overfitting phenomenon caused by small samples. Secondly， the node attention module is constructed to improve the feature extraction ability of the broad neural network under the condition of small samples. Finally， the effectiveness of the proposed method is verified on the public dataset. The results show that compared with the deep learning method with a small number of samples， the proposed method improves the overfitting phenomenon of the deep learning network， strengthens the feature extraction ability of the broad learning method， and improves the recognition accuracy.
Under the condition of few-shot specific communication emitter identification， the difficulty of extracting individual features of communication radiation source by the existing deep learning algorithm increases， and the recognition rate decreases. To solve this problem， this paper proposes a recognition method to construct a shallow neural network by fusing attention mechanism and broad learning. Firstly， broad learning is introduced to simplify the network model and reduce the overfitting phenomenon caused by small samples. Secondly， the node attention module is constructed to improve the feature extraction ability of the broad neural network under the condition of small samples. Finally， the effectiveness of the proposed method is verified on the public dataset. The results show that compared with the deep learning method with a small number of samples， the proposed method improves the overfitting phenomenon of the deep learning network， strengthens the feature extraction ability of the broad learning method， and improves the recognition accuracy.
2025, 40(3):585-602.
DOI: 10.16337/j.1004-9037.2025.03.003
Abstract:
With the rapid development of generative AI technologies， especially breakthroughs in the field of large language models （LLMs）， both academia and industry are actively seeking deeper integration between these large-scale AI models and communication networks. This paper aims to explore this emerging field in depth by reviewing the latest research advancements. It provides a comprehensive analysis of how LLMs can enhance the intelligence of communication networks and how communication networks can improve the performance of LLMs. First， the paper introduces the mainstream Transformer-based architectures of LLMs， elaborating on their training processes and the mechanism of intelligent emergence. It then analyzes the intelligent applications of LLMs in network design， diagnostics， configuration， security， network language understanding， and specification analysis， and discusses the corresponding technical implementation methods. Furthermore， the paper explores the crucial role of communication networks in supporting the training， inference， and deployment of LLMs， with a focus on distributed LLM construction technologies based on cloud-edge collaboration and multi-agent LLM network construction solutions. Finally， the paper identifies several key research challenges that remain to be addressed and provides insights into future research directions.
With the rapid development of generative AI technologies， especially breakthroughs in the field of large language models （LLMs）， both academia and industry are actively seeking deeper integration between these large-scale AI models and communication networks. This paper aims to explore this emerging field in depth by reviewing the latest research advancements. It provides a comprehensive analysis of how LLMs can enhance the intelligence of communication networks and how communication networks can improve the performance of LLMs. First， the paper introduces the mainstream Transformer-based architectures of LLMs， elaborating on their training processes and the mechanism of intelligent emergence. It then analyzes the intelligent applications of LLMs in network design， diagnostics， configuration， security， network language understanding， and specification analysis， and discusses the corresponding technical implementation methods. Furthermore， the paper explores the crucial role of communication networks in supporting the training， inference， and deployment of LLMs， with a focus on distributed LLM construction technologies based on cloud-edge collaboration and multi-agent LLM network construction solutions. Finally， the paper identifies several key research challenges that remain to be addressed and provides insights into future research directions.
2025, 40(3):699-710.
DOI: 10.16337/j.1004-9037.2025.03.011
Abstract:
Synthetic aperture radar （SAR） image targets typically exhibit subtle edge features， which can vary across different scales. Edge features provide crucial information about the shape and contour of target objects， improving the model’s localization capabilities. However， existing SAR object detection methods often underperform in learning edge features， limiting their ability to accurately perceive target edges. To address this， we propose a SAR target detection method based on edge feature guided learning （EFGL）. This approach builds upon the fully convolutional one-stage （FCOS） object detection framework and leverages edge features to guide the learning process in feature pyramid networks （FPN）. By integrating an edge operator module into FPN， the network’s capacity to learn multi-scale edge features is explicitly enhanced. Additionally， during multi-scale feature fusion， we introduce an edge feature-guided fusion module that incorporates a spatial attention mechanism to enable edge-guided fusion across adjacent feature levels. On the MSAR and SAR-Aircraft-1.0 datasets， the proposed method achieves detection accuracies of 68.68% and 67.44% under the AP’07 standard， showing improvements of 1.34% and 4.81% over the baseline network， respectively compared to other related algorithms， this method demonstrates superior target localization and overall performance in SAR target detection.
Synthetic aperture radar （SAR） image targets typically exhibit subtle edge features， which can vary across different scales. Edge features provide crucial information about the shape and contour of target objects， improving the model’s localization capabilities. However， existing SAR object detection methods often underperform in learning edge features， limiting their ability to accurately perceive target edges. To address this， we propose a SAR target detection method based on edge feature guided learning （EFGL）. This approach builds upon the fully convolutional one-stage （FCOS） object detection framework and leverages edge features to guide the learning process in feature pyramid networks （FPN）. By integrating an edge operator module into FPN， the network’s capacity to learn multi-scale edge features is explicitly enhanced. Additionally， during multi-scale feature fusion， we introduce an edge feature-guided fusion module that incorporates a spatial attention mechanism to enable edge-guided fusion across adjacent feature levels. On the MSAR and SAR-Aircraft-1.0 datasets， the proposed method achieves detection accuracies of 68.68% and 67.44% under the AP’07 standard， showing improvements of 1.34% and 4.81% over the baseline network， respectively compared to other related algorithms， this method demonstrates superior target localization and overall performance in SAR target detection.
103 Improved F-LOAM Algorithm Based on Three-Stage De-distortion and Hierarchical Downsampling Mechanism
2025, 40(5):1294-1305.
DOI: 10.16337/j.1004-9037.2025.05.015
Abstract:
The traditional fast LiDAR odometry and mapping （F-LOAM） algorithm performs a two-stage de-distortion process on the feature points， but only the first stage de-distorts the feature points， and the second-stage de-distortion is used for building the map， which leads to the lack of accuracy in the bit-position estimation. In order to solve this problem， this paper proposes an improved three-stage de-distortion mechanism combined with a voxelized grid-based hierarchical downsampling mechanism to improve the real-time performance of the algorithm. The improved F-LOAM algorithm shows excellent test results on the KITTI dataset. The three-stage de-distortion mechanism and the hierarchical downsampling strategy not only reduce the computational burden effectively， but also ensure the validity of feature points and the accuracy of the global map.
The traditional fast LiDAR odometry and mapping （F-LOAM） algorithm performs a two-stage de-distortion process on the feature points， but only the first stage de-distorts the feature points， and the second-stage de-distortion is used for building the map， which leads to the lack of accuracy in the bit-position estimation. In order to solve this problem， this paper proposes an improved three-stage de-distortion mechanism combined with a voxelized grid-based hierarchical downsampling mechanism to improve the real-time performance of the algorithm. The improved F-LOAM algorithm shows excellent test results on the KITTI dataset. The three-stage de-distortion mechanism and the hierarchical downsampling strategy not only reduce the computational burden effectively， but also ensure the validity of feature points and the accuracy of the global map.
104 Channel Estimation Based on Global Super-Resolution Denoising in Non-terrestrial Network Scenarios
2025, 40(6):1424-1433.
DOI: 10.16337/j.1004-9037.2025.06.004
Abstract:
In non-terrestrial network （NTN） scenarios， to overcome the effect of large Doppler frequency offset on the communication， a channel estimation method based on global information super resolution denoising neural network （GSRDnNet） is proposed. This method considers the channel estimation matrix at the pilot obtained by the least square（LS） estimation algorithm as a low-resolution small-size image and takes it as the input to the neural network. The input data is then processed by the GSRDnNet network to obtain a more accurate high-resolution image with a complete channel response matrix for the time-frequency resource block. Four NTN-tapped delay line （TDL） A，B，C and D channel models are used for simulation verification. Simulation results indicate that GSRDnNet improves mean squared error （MSE） performance by 3.37—8.83 dB compared to the traditional LS algorithm. Compared with the practical channel estimation（PCE） algorithm， the MSE is improved by 2.11—6.06 dB， and compared with the SRCNN+DnCNN method， which requires pre-interpolation processing， the MSE is improved by 1.37—4.40 dB. And compared with super resolution convolutional neural network （SRCNN）+denoising convolutional neural network （DnCNN） ，the input of GSRDnNet network model only considers the channel estimation matrix at the pilot， so it not only has higher estimation accuracy， but also reduces the computational complexity by about 84%.
In non-terrestrial network （NTN） scenarios， to overcome the effect of large Doppler frequency offset on the communication， a channel estimation method based on global information super resolution denoising neural network （GSRDnNet） is proposed. This method considers the channel estimation matrix at the pilot obtained by the least square（LS） estimation algorithm as a low-resolution small-size image and takes it as the input to the neural network. The input data is then processed by the GSRDnNet network to obtain a more accurate high-resolution image with a complete channel response matrix for the time-frequency resource block. Four NTN-tapped delay line （TDL） A，B，C and D channel models are used for simulation verification. Simulation results indicate that GSRDnNet improves mean squared error （MSE） performance by 3.37—8.83 dB compared to the traditional LS algorithm. Compared with the practical channel estimation（PCE） algorithm， the MSE is improved by 2.11—6.06 dB， and compared with the SRCNN+DnCNN method， which requires pre-interpolation processing， the MSE is improved by 1.37—4.40 dB. And compared with super resolution convolutional neural network （SRCNN）+denoising convolutional neural network （DnCNN） ，the input of GSRDnNet network model only considers the channel estimation matrix at the pilot， so it not only has higher estimation accuracy， but also reduces the computational complexity by about 84%.
2025, 40(6):1434-1444.
DOI: 10.16337/j.1004-9037.2025.06.005
Abstract:
This paper studies the precoding optimization problem for extremely large-scale multiple-input-multiple-output（XL-MIMO） downlink systems under a near-field channel model based on spectral efficiency fairness. The near-field channel model considers the coexistence of line-of-sight （LOS） and non LOS （NLOS） non-stationary mixed channels within the cell， where LOS channels are modeled using spherical wave models， while NLOS channels are modeled using Rayleigh models. The geometric mean of spectral efficiency is used as the optimization target to ensure fairness among users and optimize the overall spectral efficiency of the system. To handle the complex optimization objective function， a first-order Taylor expansion approximation is applied to create a simplified objective function. Subsequently， Lagrangian dual transformation and quadratic transformations are used to transform the original optimization problem into an equivalent one that is easier to solve. Finally， to reduce computational complexity， the projection fast iterative shrinkage threshold algorithm （PFISTA）， which combines fast iterative shrinkage thresholding algorithms with projected gradient descent， is employed to solve the equivalent optimization problem. Simulation results show that using the geometric mean as the objective function can reduce differences in spectral efficiencies among users， leading to a balanced improvement in user spectral efficiencies. Moreover， PFISTA achieves comparable performance to existing methods while maintaining lower computational complexity.
This paper studies the precoding optimization problem for extremely large-scale multiple-input-multiple-output（XL-MIMO） downlink systems under a near-field channel model based on spectral efficiency fairness. The near-field channel model considers the coexistence of line-of-sight （LOS） and non LOS （NLOS） non-stationary mixed channels within the cell， where LOS channels are modeled using spherical wave models， while NLOS channels are modeled using Rayleigh models. The geometric mean of spectral efficiency is used as the optimization target to ensure fairness among users and optimize the overall spectral efficiency of the system. To handle the complex optimization objective function， a first-order Taylor expansion approximation is applied to create a simplified objective function. Subsequently， Lagrangian dual transformation and quadratic transformations are used to transform the original optimization problem into an equivalent one that is easier to solve. Finally， to reduce computational complexity， the projection fast iterative shrinkage threshold algorithm （PFISTA）， which combines fast iterative shrinkage thresholding algorithms with projected gradient descent， is employed to solve the equivalent optimization problem. Simulation results show that using the geometric mean as the objective function can reduce differences in spectral efficiencies among users， leading to a balanced improvement in user spectral efficiencies. Moreover， PFISTA achieves comparable performance to existing methods while maintaining lower computational complexity.
2025, 40(6):1527-1537.
DOI: 10.16337/j.1004-9037.2025.06.012
Abstract:
To address the low recognition rates due to the insufficient utilization of original signal timing information in automatic modulation recognition (AMR)， this paper proposes a signal pattern recognition algorithm based on frequency domain denoising and temporal convolutional networks （TCN）. Experiments are conducted using the standard dataset RML2016.10a， and a frequency domain denoising module （FDDM） is introduced to effectively suppress environmental noise. The I/ Q components of the signal are converted into A/ P components， followed by vector normalization to enhance stability. Finally， the preprocessed signals are fed into the TCN network for classification recognition. Results indicate that this algorithm achieves an average recognition rate higher than those of models such as gated recurrent unit （GRU）， convolutional long short-term memory （LSTM）， memory-cost-efficient convolutional neural network （MCNet）， cost-efficient hybrid deep learning network （CGDNet）， and denoising auto-encoder （DAE） when processing complex modulation schemes like 16 QAM and 64 QAM. Additionally， validation using actual I/Q data collected through the universal software radio peripheral （USRP） demonstrates that the algorithm exhibits good robustness and application potential under additive white Gaussian noise （AWGN） channels.
To address the low recognition rates due to the insufficient utilization of original signal timing information in automatic modulation recognition (AMR)， this paper proposes a signal pattern recognition algorithm based on frequency domain denoising and temporal convolutional networks （TCN）. Experiments are conducted using the standard dataset RML2016.10a， and a frequency domain denoising module （FDDM） is introduced to effectively suppress environmental noise. The I
2025, 40(6):1445-1463.
DOI: 10.16337/j.1004-9037.2025.06.006
Abstract:
To cope with the challenges of low communication rate and susceptibility to interception of sensing-centric waveforms in integrated sensing and communication （ISAC） systems， this paper designs a multi-channel frequency-hopping transmission architecture based on quadrature phase shift keying （QPSK） and linear frequency modulation （LFM） signals. This architecture transmits multiple LFM signals simultaneously within overlapping spectral bands to enhance the symbol rate. Encrypted communication is achieved by the frequency-hopping characteristics of the LFM subcarriers. Furthermore， the time-division multiplexing （TDM） mechanism of dynamic preambles and data improves the accuracy of path indexing and parameter estimation for multi-path LFM signals. Simulation results show that under identical symbol rate constraints， the proposed multi-channel parallel architecture achieves superior bit error rate （BER） performance compared to traditional single-channel schemes. Specifically， the BER of the four-channel architecture at 0 dB SNR is reduced by one order of magnitude relative to the single-channel scheme. Additionally， the dynamic preamble scheme meets the requirements for path index identification across various scenarios. At 0 dB SNR， the normalized mean square error（NMSE） remains below10 - 2 ![]()
![]()
10 - 2 ![]()
![]()
0 d B ![]()
![]()
To cope with the challenges of low communication rate and susceptibility to interception of sensing-centric waveforms in integrated sensing and communication （ISAC） systems， this paper designs a multi-channel frequency-hopping transmission architecture based on quadrature phase shift keying （QPSK） and linear frequency modulation （LFM） signals. This architecture transmits multiple LFM signals simultaneously within overlapping spectral bands to enhance the symbol rate. Encrypted communication is achieved by the frequency-hopping characteristics of the LFM subcarriers. Furthermore， the time-division multiplexing （TDM） mechanism of dynamic preambles and data improves the accuracy of path indexing and parameter estimation for multi-path LFM signals. Simulation results show that under identical symbol rate constraints， the proposed multi-channel parallel architecture achieves superior bit error rate （BER） performance compared to traditional single-channel schemes. Specifically， the BER of the four-channel architecture at 0 dB SNR is reduced by one order of magnitude relative to the single-channel scheme. Additionally， the dynamic preamble scheme meets the requirements for path index identification across various scenarios. At 0 dB SNR， the normalized mean square error（NMSE） remains below
2025, 40(6):1477-1489.
DOI: 10.16337/j.1004-9037.2025.06.008
Abstract:
To solve the fast pairing and power allocation problem of non-orthogonal multiple access （NOMA） under imperfect serial interference cancellation （SIC） conditions， a deep reinforcement learning-based user pairing and power optimization scheme is proposed. First， this paper considers the scenario of imperfect SIC for multiuser NOMA， and constructs an optimization problem to maximize the system reachable communication rate with user pairing and user transmit power allocation factor as optimization variables. The condition of user pairing using NOMA under the imperfect SIC condition is analyzed， and the user power allocation for the maximum reachable rate under this condition is introduced. Second， the user pairing problem is treated as a combinatorial optimization problem， and a novel user pairing scheme is designed based on the real-time requirement using an improved pointer network. Simulation results show that this scheme can effectively improve the reachable rate of the NOMA system to 99.8% of that of the optimal exhaustive search algorithm. It achieves real-time performance and adapts to the dynamic change of the number of users.
To solve the fast pairing and power allocation problem of non-orthogonal multiple access （NOMA） under imperfect serial interference cancellation （SIC） conditions， a deep reinforcement learning-based user pairing and power optimization scheme is proposed. First， this paper considers the scenario of imperfect SIC for multiuser NOMA， and constructs an optimization problem to maximize the system reachable communication rate with user pairing and user transmit power allocation factor as optimization variables. The condition of user pairing using NOMA under the imperfect SIC condition is analyzed， and the user power allocation for the maximum reachable rate under this condition is introduced. Second， the user pairing problem is treated as a combinatorial optimization problem， and a novel user pairing scheme is designed based on the real-time requirement using an improved pointer network. Simulation results show that this scheme can effectively improve the reachable rate of the NOMA system to 99.8% of that of the optimal exhaustive search algorithm. It achieves real-time performance and adapts to the dynamic change of the number of users.
109 New Target Parameter Estimation Algorithms for Integrated Sensing and Communication Based on OTFS
2025, 40(6):1412-1423.
DOI: 10.16337/j.1004-9037.2025.06.003
Abstract:
In the terahertz frequency band， utilizing an orthogonal time frequency space （OTFS）-integrated sensing and communication （ISAC） vehicular networking system with superimposed pilots can achieve high-speed data transmission between vehicles alongside high-precision parameter sensing. First， the OTFS signal modulation model， the communication signal model， and the sensing signal model are established and analyzed. At the receiver， a new sensing channel model containing angle information is derived. Subsequently， a coarse angle estimation algorithm based on the uniform grid multiple signal classification （MUSIC） method and a fine angle estimation algorithm based on the golden section MUSIC method are proposed. Finally， a maximum likelihood estimation criterion-based algorithm is proposed for estimating the integer and fractional parts of the channel delay and Doppler shift parameters. Simulation results demonstrate that the proposed algorithms can achieve accurate estimation of sensing parameters such as angle， distance， and velocity.
In the terahertz frequency band， utilizing an orthogonal time frequency space （OTFS）-integrated sensing and communication （ISAC） vehicular networking system with superimposed pilots can achieve high-speed data transmission between vehicles alongside high-precision parameter sensing. First， the OTFS signal modulation model， the communication signal model， and the sensing signal model are established and analyzed. At the receiver， a new sensing channel model containing angle information is derived. Subsequently， a coarse angle estimation algorithm based on the uniform grid multiple signal classification （MUSIC） method and a fine angle estimation algorithm based on the golden section MUSIC method are proposed. Finally， a maximum likelihood estimation criterion-based algorithm is proposed for estimating the integer and fractional parts of the channel delay and Doppler shift parameters. Simulation results demonstrate that the proposed algorithms can achieve accurate estimation of sensing parameters such as angle， distance， and velocity.
2025, 40(6):1382-1411.
DOI: 10.16337/j.1004-9037.2025.06.002
Abstract:
With the increasing of array antennas and the growing complexity of anti-jamming， traditional adaptive beamforming methods often suffer from high computational complexity. Deep learning， with its powerful data-driven capabilities， offers a novel approach to overcoming the performance bottlenecks of traditional adaptive beamforming. This paper provides a systematic review on current studies and development trends of deep learning in array antenna beamforming. First， we revisit the evolution of traditional beamforming algorithms，ranging from the Howells-Applebaum adaptive processor to robust beamforming based on convex optimization. Second， we analyze the innovative applications of deep learning models such as convolutional neural networks （CNNs）， recurrent neural networks （RNNs）， and long short-term memory （LSTM） networks in beamforming. This review demonstrates that deep learning methods exhibit significant advantages in improving system performance due to their powerful nonlinear modeling capabilities， end-to-end optimization characteristics， and environmental adaptability. Specifically， in mobile communications， deep learning-based beamforming methods substantially enhance the computational efficiency and environmental adaptability of massive multiple input multiple output （MIMO） systems. In radar signal processing， deep learning models effectively improve anti-jamming performance and target detection accuracy. In acoustic signal processing， deep neural networks enable more precise sound source localization and noise suppression. Finally，this paper explores future research directions， including network architecture innovation， real-time processing optimization， robustness enhancement， cross-scenario transfer learning， theoretical foundation deepening， and novel application expansion.
With the increasing of array antennas and the growing complexity of anti-jamming， traditional adaptive beamforming methods often suffer from high computational complexity. Deep learning， with its powerful data-driven capabilities， offers a novel approach to overcoming the performance bottlenecks of traditional adaptive beamforming. This paper provides a systematic review on current studies and development trends of deep learning in array antenna beamforming. First， we revisit the evolution of traditional beamforming algorithms，ranging from the Howells-Applebaum adaptive processor to robust beamforming based on convex optimization. Second， we analyze the innovative applications of deep learning models such as convolutional neural networks （CNNs）， recurrent neural networks （RNNs）， and long short-term memory （LSTM） networks in beamforming. This review demonstrates that deep learning methods exhibit significant advantages in improving system performance due to their powerful nonlinear modeling capabilities， end-to-end optimization characteristics， and environmental adaptability. Specifically， in mobile communications， deep learning-based beamforming methods substantially enhance the computational efficiency and environmental adaptability of massive multiple input multiple output （MIMO） systems. In radar signal processing， deep learning models effectively improve anti-jamming performance and target detection accuracy. In acoustic signal processing， deep neural networks enable more precise sound source localization and noise suppression. Finally，this paper explores future research directions， including network architecture innovation， real-time processing optimization， robustness enhancement， cross-scenario transfer learning， theoretical foundation deepening， and novel application expansion.
2025, 40(6):1464-1476.
DOI: 10.16337/j.1004-9037.2025.06.007
Abstract:
Due to the varying distances of different unmanned aerial vehicles （UAVs）， the overlapping signals often exhibit different signal-to-noise ratios， and the presence of various interference signals in low-altitude environments further increases the difficulty of identification. To address these problems， this paper proposes a joint detection-separation-identification scheme for overlapping signals from multiple UAVs. The scheme effectively improves the detection and identification performance of overlapping signals with different SNRs through three steps： signal detection， signal separation， and signal identification. First， the YOLO detector is employed to locate potential UAV signals on the time-frequency spectrogram. Then， a data augmentation method based on random deviation is proposed to mitigate the bias in the signal separation process. Subsequently， the bandwidth and duration features of the signals are extracted using a YOLO-based classifier to achieve classification of distinct UAV signals. Finally， to further improve the recognition accuracy of signals from identical UAV models， an enhanced ResNet model integrated with attention mechanisms and an optimized Bagging ensemble learning method are proposed. Experimental results based on publicly available datasets demonstrate that the proposed scheme outperforms existing methods in scenarios where interference signals and UAVs of the same model coexist.
Due to the varying distances of different unmanned aerial vehicles （UAVs）， the overlapping signals often exhibit different signal-to-noise ratios， and the presence of various interference signals in low-altitude environments further increases the difficulty of identification. To address these problems， this paper proposes a joint detection-separation-identification scheme for overlapping signals from multiple UAVs. The scheme effectively improves the detection and identification performance of overlapping signals with different SNRs through three steps： signal detection， signal separation， and signal identification. First， the YOLO detector is employed to locate potential UAV signals on the time-frequency spectrogram. Then， a data augmentation method based on random deviation is proposed to mitigate the bias in the signal separation process. Subsequently， the bandwidth and duration features of the signals are extracted using a YOLO-based classifier to achieve classification of distinct UAV signals. Finally， to further improve the recognition accuracy of signals from identical UAV models， an enhanced ResNet model integrated with attention mechanisms and an optimized Bagging ensemble learning method are proposed. Experimental results based on publicly available datasets demonstrate that the proposed scheme outperforms existing methods in scenarios where interference signals and UAVs of the same model coexist.
2025, 40(5):1239-1249.
DOI: 10.16337/j.1004-9037.2025.05.010
Abstract:
With the rapid development of mobile communication technology， wireless networks are facing multiple challenges， including resource allocation， traffic analysis， and 6G base station optimization. Effective prediction of wireless network traffic helps to allocate network resources reasonably and provides users with more stable and efficient services， ensuring network performance. To solve the problem of low prediction accuracy in the current wireless network traffic predictions due to insufficient mining of spatial and temporal features， this paper conducts research on intelligent traffic prediction algorithms based on deep learning methods， and proposes a prediction algorithm based on graph convolutional network-long short-term memory (GCN-LSTM） model. Experimental results show that the accuracy of this algorithm is 84.71% in actual network applications， which is superior to other deep learning-based traffic prediction methods， providing strong support for the rational allocation of 6G network resources and efficient service.
With the rapid development of mobile communication technology， wireless networks are facing multiple challenges， including resource allocation， traffic analysis， and 6G base station optimization. Effective prediction of wireless network traffic helps to allocate network resources reasonably and provides users with more stable and efficient services， ensuring network performance. To solve the problem of low prediction accuracy in the current wireless network traffic predictions due to insufficient mining of spatial and temporal features， this paper conducts research on intelligent traffic prediction algorithms based on deep learning methods， and proposes a prediction algorithm based on graph convolutional network-long short-term memory (GCN-LSTM） model. Experimental results show that the accuracy of this algorithm is 84.71% in actual network applications， which is superior to other deep learning-based traffic prediction methods， providing strong support for the rational allocation of 6G network resources and efficient service.
2025, 40(5):1270-1282.
DOI: 10.16337/j.1004-9037.2025.05.013
Abstract:
Multiple-input-multiple-output （MIMO） and non-orthogonal multiple access （NOMA） techniques are widely applied to unmanned aerial vehicle （UAV） communications due to their superior spectral efficiency. UAVs can serve as relays to provide flexible and reliable connections for users， thereby adding significant research value. To overcome the problems of multi-user interference and clustering in the MIMO-NOMA UAV relay networks， a new downlink transmission model based on amplify-and-forward （AF） is proposed. First， a three-dimensional stochastic geometry framework is applied to user clustering， and an AF-based precoding scheme is proposed according to the NOMA principle. Second， the analytical expressions for the outage probability （OP） of paired users are derived following the statistics of the equivalent propagation channel of AF relay transmitting model. And the asymptotic results and diversity order of OP under high signal-to-noise ratio （SNR） are obtained using the first-order Taylor expansion. Finally， simulations validate the theoretical analysis results through the impact of key variables on OP performance. Additionally， compared with existing transmission schemes in MIMO-NOMA， the proposed scheme can effectively improve the OP performance in multiple user UAV relay networks.
Multiple-input-multiple-output （MIMO） and non-orthogonal multiple access （NOMA） techniques are widely applied to unmanned aerial vehicle （UAV） communications due to their superior spectral efficiency. UAVs can serve as relays to provide flexible and reliable connections for users， thereby adding significant research value. To overcome the problems of multi-user interference and clustering in the MIMO-NOMA UAV relay networks， a new downlink transmission model based on amplify-and-forward （AF） is proposed. First， a three-dimensional stochastic geometry framework is applied to user clustering， and an AF-based precoding scheme is proposed according to the NOMA principle. Second， the analytical expressions for the outage probability （OP） of paired users are derived following the statistics of the equivalent propagation channel of AF relay transmitting model. And the asymptotic results and diversity order of OP under high signal-to-noise ratio （SNR） are obtained using the first-order Taylor expansion. Finally， simulations validate the theoretical analysis results through the impact of key variables on OP performance. Additionally， compared with existing transmission schemes in MIMO-NOMA， the proposed scheme can effectively improve the OP performance in multiple user UAV relay networks.
2025, 40(5):1250-1260.
DOI: 10.16337/j.1004-9037.2025.05.011
Abstract:
Specific emitter identification （SEI） refers to the technique of distinguishing emitters by utilizing unique and subtle features in received electromagnetic signals. Due to its powerful feature extraction ability， deep learning has gradually become the main means of implementing SEI. However， in non-cooperative scenarios， labeled samples generally cannot be obtained to train the neural network， and the number of emitters to be identified is unknown. Therefore， this paper proposes an unsupervised SEI method based on directed graph connectivity without specifying the number of emitters. Drawing inspiration from the idea of hierarchical clustering， the radio frequency fingerprinting feature space is first divided into multiple sub-clusters based on local density， and the relationship between feature vectors is mapped to a directed graph. Then， based on the connectivity of the directed graph， the multiple subclusters are automatically merged to obtain the final identification result. Experimental results show that under low signal-to-noise ratio conditions， the proposed method can accurately identify individual emitters， and its identification performance is improved by 7.1%—53.1% compared to the benchmark algorithms.
Specific emitter identification （SEI） refers to the technique of distinguishing emitters by utilizing unique and subtle features in received electromagnetic signals. Due to its powerful feature extraction ability， deep learning has gradually become the main means of implementing SEI. However， in non-cooperative scenarios， labeled samples generally cannot be obtained to train the neural network， and the number of emitters to be identified is unknown. Therefore， this paper proposes an unsupervised SEI method based on directed graph connectivity without specifying the number of emitters. Drawing inspiration from the idea of hierarchical clustering， the radio frequency fingerprinting feature space is first divided into multiple sub-clusters based on local density， and the relationship between feature vectors is mapped to a directed graph. Then， based on the connectivity of the directed graph， the multiple subclusters are automatically merged to obtain the final identification result. Experimental results show that under low signal-to-noise ratio conditions， the proposed method can accurately identify individual emitters， and its identification performance is improved by 7.1%—53.1% compared to the benchmark algorithms.
2025, 40(4):997-1010.
DOI: 10.16337/j.1004-9037.2025.04.013
Abstract:
For the multi-user scenarios of the high-altitude platform （HAP）-assisted integrated satellite and aerial network， this paper proposes a novel hybrid wireless transmission scheme on terahertz （THz） and millimeter-wave （mmWave） bands， aiming at providing reliable access for heterogeneous users and solving the problem of capacity constraints of the satellite backbone network. Firstly， according to the relevance and service priorities among users， terrestrial users are divided into two groups， each of which includes a primary user and a secondary user. The secondary user adopts the cognitive radio-based non-orthogonal multiple access （CR-NOMA） technique to control its transmit power without compromising the quality of service （QoS） of the primary user， while the inter-group interference is eliminated by the zero-forcing （ZF） based beamforming technique. Secondly， it is assumed that the channel from each user to HAP experiences mmWave band with Nakagami-m distribution， while the channel from HAP to satellite experiences THz band withα - μ ![]()
![]()
For the multi-user scenarios of the high-altitude platform （HAP）-assisted integrated satellite and aerial network， this paper proposes a novel hybrid wireless transmission scheme on terahertz （THz） and millimeter-wave （mmWave） bands， aiming at providing reliable access for heterogeneous users and solving the problem of capacity constraints of the satellite backbone network. Firstly， according to the relevance and service priorities among users， terrestrial users are divided into two groups， each of which includes a primary user and a secondary user. The secondary user adopts the cognitive radio-based non-orthogonal multiple access （CR-NOMA） technique to control its transmit power without compromising the quality of service （QoS） of the primary user， while the inter-group interference is eliminated by the zero-forcing （ZF） based beamforming technique. Secondly， it is assumed that the channel from each user to HAP experiences mmWave band with Nakagami-m distribution， while the channel from HAP to satellite experiences THz band with
2025, 40(3):774-783.
DOI: 10.16337/j.1004-9037.2025.03.016
Abstract:
This paper addresses a wireless control system where multiple control loops share spectrum resources and proposes a random access scheme based on imperfect channel state information to achieve control stability. Firstly， in the scenario where multiple control loops access the remote controller with a fixed probability， control stability conditions are derived through the Lyapunov function. Secondly， an optimization problem is formulated with access probability and transmission power as variables， aiming to minimize the total energy consumption of the system while satisfying control stability and transmission power constraints. Since this optimization problem is non-convex and only imperfect channel state information（CSI） can be obtained， a design approach for the access strategy is proposed by combining the Lyapunov stability theorem with mathematical methods such as Bernstein inequality and continuous convex approximation. Simulation results demonstrate that the proposed scheme， compared to existing typical access solutions， can significantly reduce system energy consumption while ensuring control performance.
This paper addresses a wireless control system where multiple control loops share spectrum resources and proposes a random access scheme based on imperfect channel state information to achieve control stability. Firstly， in the scenario where multiple control loops access the remote controller with a fixed probability， control stability conditions are derived through the Lyapunov function. Secondly， an optimization problem is formulated with access probability and transmission power as variables， aiming to minimize the total energy consumption of the system while satisfying control stability and transmission power constraints. Since this optimization problem is non-convex and only imperfect channel state information（CSI） can be obtained， a design approach for the access strategy is proposed by combining the Lyapunov stability theorem with mathematical methods such as Bernstein inequality and continuous convex approximation. Simulation results demonstrate that the proposed scheme， compared to existing typical access solutions， can significantly reduce system energy consumption while ensuring control performance.
2025, 40(1):27-44.
DOI: 10.16337/j.1004‐9037.2025.01.003
Abstract:
The low-altitude intelligent network， as a new type of productivity， has facilitated the rapid development of the low-altitude economy. However， the widespread application of unmanned aerial vehicles （UAVs） has posed significant challenges for airspace regulation. This paper mainly focuses on the performance analysis of two potential UAV flight regulation technologies applied to the low-altitude intelligent network： Automatic dependent surveillance-broadcast （ADS-B） and remote identification （Remote ID）. Firstly， we systematically introduce the basic mechanisms of ADS-B and Remote ID. Then， based on current technical standards， theoretical transmission distances of these two technologies are analyzed， and methods for evaluating positioning accuracy are defined. We build ADS-B and Remote ID experimental systems that meet performance requirements， estimate the actual transmission distances through measured signal strength， and measure the positioning accuracy of latitude， longitude， and altitude， as well as the packet loss rate. Through the analysis of the measured data， this paper comprehensively evaluates practical application effects of ADS-B and Remote ID in low-altitude intelligent network for the first time. Results show that ADS-B outperforms Remote ID in terms of transmission range and positioning accuracy， while Remote ID performs better in altitude positioning. In terms of communication stability， ADS-B provides stable reception over long distances， while Remote ID performs well in short-range scenarios. Finally， the future development directions of UAV regulation technology are discussed， and solutions for optimizing transmission distance， coverage range， positioning accuracy， and packet loss rate are proposed.
The low-altitude intelligent network， as a new type of productivity， has facilitated the rapid development of the low-altitude economy. However， the widespread application of unmanned aerial vehicles （UAVs） has posed significant challenges for airspace regulation. This paper mainly focuses on the performance analysis of two potential UAV flight regulation technologies applied to the low-altitude intelligent network： Automatic dependent surveillance-broadcast （ADS-B） and remote identification （Remote ID）. Firstly， we systematically introduce the basic mechanisms of ADS-B and Remote ID. Then， based on current technical standards， theoretical transmission distances of these two technologies are analyzed， and methods for evaluating positioning accuracy are defined. We build ADS-B and Remote ID experimental systems that meet performance requirements， estimate the actual transmission distances through measured signal strength， and measure the positioning accuracy of latitude， longitude， and altitude， as well as the packet loss rate. Through the analysis of the measured data， this paper comprehensively evaluates practical application effects of ADS-B and Remote ID in low-altitude intelligent network for the first time. Results show that ADS-B outperforms Remote ID in terms of transmission range and positioning accuracy， while Remote ID performs better in altitude positioning. In terms of communication stability， ADS-B provides stable reception over long distances， while Remote ID performs well in short-range scenarios. Finally， the future development directions of UAV regulation technology are discussed， and solutions for optimizing transmission distance， coverage range， positioning accuracy， and packet loss rate are proposed.
2025, 40(1):56-71.
DOI: 10.16337/j.1004-9037.2025.01.005
Abstract:
Aiming at the problems of beam training and target localization and tracking in millimeter-wave （mmWave） low-altitude unmanned aerial vehicle （UAV） scenarios， inspired by information theory， this paper proposes a hierarchical beam training algorithm based on the channel coding principle and a UAV target localization and tracking algorithm based on mmWave radar sensing， respectively. The proposed algorithms have high generalization and robustness， and are applicable not only to static and dynamic scenarios， but also to far-field， near-field， reconfigurable intelligent surface （RIS） assisted communication， and distributed cellular-free network scenarios， as well as illegal UAV intrusion sensing， etc. The algorithms are also validated through simulations and hardware platform tests. Specifically， the channel coding beam training algorithm can significantly improve the training accuracy by using coding gain and error correction mechanism. The mmWave radar algorithm combines Capon beam formation， constant false alarm rate （CFAR） detection and density-based spatial clustering of applications with noise （DBScan） to achieve UAV detection and tracking. Both simulation and hardware test results show that these algorithms can effectively improve the efficiency of beam training and the accuracy of sensing and localization in mmWave low-altitude UAV scenarios， providing technical support for the further prosperous development of low-altitude economy.
Aiming at the problems of beam training and target localization and tracking in millimeter-wave （mmWave） low-altitude unmanned aerial vehicle （UAV） scenarios， inspired by information theory， this paper proposes a hierarchical beam training algorithm based on the channel coding principle and a UAV target localization and tracking algorithm based on mmWave radar sensing， respectively. The proposed algorithms have high generalization and robustness， and are applicable not only to static and dynamic scenarios， but also to far-field， near-field， reconfigurable intelligent surface （RIS） assisted communication， and distributed cellular-free network scenarios， as well as illegal UAV intrusion sensing， etc. The algorithms are also validated through simulations and hardware platform tests. Specifically， the channel coding beam training algorithm can significantly improve the training accuracy by using coding gain and error correction mechanism. The mmWave radar algorithm combines Capon beam formation， constant false alarm rate （CFAR） detection and density-based spatial clustering of applications with noise （DBScan） to achieve UAV detection and tracking. Both simulation and hardware test results show that these algorithms can effectively improve the efficiency of beam training and the accuracy of sensing and localization in mmWave low-altitude UAV scenarios， providing technical support for the further prosperous development of low-altitude economy.
2025, 40(1):45-55.
DOI: 10.16337/j.1004-9037.2025.01.004
Abstract:
The low-altitude intelligent network （LAIN） serves as a fundamental infrastructure for the development of the low-altitude economy， and spectrum management and control （SMC） is one of the key technologies to address the problems of illegal spectrum use and malicious attacks in LAIN， as well as to improve the utilization rate of spectrum resources. Embodied artificial intelligence （EAI）， as a key research direction in the new generation of artificial intelligence， offers new possibilities for the development of SMC for LAIN due to its characteristics of physical embodiment， environmental interaction， and intelligent growth. Firstly， this paper introduces the requirements of SMC for LAIN from the aspects of technical framework， research status， and main challenges. Secondly， by sorting out the connotation and advantages of EAI， the concept and significance of EAI-enabled SMC for LAIN are analyzed. Furthermore， based on the closed-loop structure of “perception-decision-action-feedback”， a technology of SMC for LAIN is proposed， which includes low-altitude embodied spectrum sensing， inferring and decision-making， and action and feedback. This provides a possible technical pathway for achieving efficient and secure SMC for LAIN.
The low-altitude intelligent network （LAIN） serves as a fundamental infrastructure for the development of the low-altitude economy， and spectrum management and control （SMC） is one of the key technologies to address the problems of illegal spectrum use and malicious attacks in LAIN， as well as to improve the utilization rate of spectrum resources. Embodied artificial intelligence （EAI）， as a key research direction in the new generation of artificial intelligence， offers new possibilities for the development of SMC for LAIN due to its characteristics of physical embodiment， environmental interaction， and intelligent growth. Firstly， this paper introduces the requirements of SMC for LAIN from the aspects of technical framework， research status， and main challenges. Secondly， by sorting out the connotation and advantages of EAI， the concept and significance of EAI-enabled SMC for LAIN are analyzed. Furthermore， based on the closed-loop structure of “perception-decision-action-feedback”， a technology of SMC for LAIN is proposed， which includes low-altitude embodied spectrum sensing， inferring and decision-making， and action and feedback. This provides a possible technical pathway for achieving efficient and secure SMC for LAIN.
