Abstract:With the rapid development of the mobile Internet a nd the Internet of Things, the number of personal wireless devices has grown exp o nentially, result ing in the increase of massive spectrum data. Therefore, the bi g spectrum data are literally formed. Meanwhile, the spect rum deficit is also increasingly precarious. Effective big spectrum data process ing is significant in improving the spectrum utilization . Firstly, fr om a perspective of wireless communication, a definition of big spectrum data is presented and its characteristics are also analyzed. Th en, p romising machine learning methods to analyze and utilize the big spectrum data are summarized, such as, the distributed and parallel learning, extreme lea rning machine, kernel b a sed learning, deep learning, reinforcement learning, game learning, and transfer learning. Finally, several open issues and research trends are addressed.

Abstract:As the long term evolution (LTE) of the fourth generation mobile communication system (4G) is succe ssfully commercialized globally and mobile data are increase d explosively, the research and development on the fifth generation of mobile communication system (5G) become the focus of both industry and acad e mia. Here, the two drivers to develop 5G are firstly analyzed. Then th e international telecommunication union(ITU) timeline for global standardization of 5G is presented and the most recent updates are described. Subsequently, widely recognized key technologies for 5G a re pres e nted. Specifically, key wireless network technologies, including the ultra dense cel lular network (UDN) and the centralized radio access network, are described in d etai l. Finally, conclusions are drawn and open problems are discussed.

Abstract:With the rapid development of aviation communications and the increasing num ber of airborne terminals, it is highly demanded to build up air to air wideband ad hoc networks supporting ultra high speed mobility both in civil and military avia tion systems . Here, the present situation and the developmen t t rend of aviation communication networks are briefly outlined. On the basis of it, the challenging issues of constructing such networks are presented, including the support of ultra high speed mobility, high data transmission rate, long distance single hop links, large scale Ad hoc networking, multiple user ac cess mechanism, as well as their restricted relationship. Finally, physical laye r key technologies and the related research progress for wideband airborne Ad ho c networks are reviewed, including modulation technology, peak to average power ratio suppression in OFDM, frequency offset estimation, channel estimation and c hannel modeling.

Abstract:Digital fountain codes are an effectiv e kind of rateless codes to avoid “feedback storm” in network broadcast/multic ast with a wide horizon of development in the network transmission of big data. Therefore, the recent research progress of digital fountain codes is summerized and some typical digital fountain codes are intro duced in different c hannels. Since degree distributions are fundamental to design digita l fountain codes, previous work about optimization design of degree distribut i ons is summarized and some linear optimization techniques over different channel s based on density evolution are presented in particular. Meanwhile, the problem s of optimization design are pointed out and some possible improvements are proposed. Finally, the outlook of optimization design in degree distributions and a p plications of digital fountain codes is analyzed.

Abstract:Satellite navigation message is an indispensable element for satellite navigation system functioning normally and precisely. The design a nd evaluation of the message is critical for constructing a satel lite navigation system. The development history and performance discrepancy regarding extant differen t GNSS are analyzed in message content, message structure， and message broadcas t way. In addition, the technical problems of design and evaluation in current navig ation message are proposed. Furthermore, the relevant technologies a nd trends are predicted and prospected.

Abstract:In multi user MIMO downlink with multi ante nnas at user terminals, the block diagonalization (BD) precoder obtains a bette r sum rate performance than matched filter (MF) and zero forcing (ZF), thus be comeing an attractive precoder. However, the conventional BD generates the null sp ace for all channel matrices excluding the current user by using s ingular value decomposition (SVD) and requires float point operations (FLOPs) o f order O(N2). As the antennas number N at ba se station (BS) tends to be a large scale one, computational complexity increas es sharply o n BS. Thus, a novel low complexity implementation for BD with a compl exity of order O(N) FLOPs is proposed, where the null space of other users′ c hannel matrices is constructed by a projection matrix. Simulation and ana lysis exhibit that the proposed low complexity BD significantly reduce the compl e xity, compared with the conventional BD and regularized block diagonalization(RB D) at the cost of a slight sum rate loss. The algorithm performs better than MF and ZF. Meanwhile, the sum rat e performance is similar to the conventional BD and SVD as N app roaches a large scale number.

Abstract:Physical layer security (PLS) is an effective means to protect communications against eavesdropping attacks by e xpl oiting physical characteristics of wireless channels. As the information and com munication technology (ICT) has been developed for decades, PLS attracts mo re and more attention of researchers. Here, PLS performance of the join t multi user s cheduling and the cooperative jammer sele c tion is explored. With the combination of the two techniques, the wireless c ommu nica tion security is investigated. In a wireless communication system wit h multiple users, multi user scheduling schemes are adopted to choose one legi t imate user of the best channel to transmit signals. Meanwhile, the eav esdroppers are interfered to keep the transmissions between BS and legi timate users confidential for avoiding the interference to the legitimate u sers. CJ signal is designed so that nulls are formed at the legitimate users . Simulatio n results show that the joint multi user scheduling and cooperative j a mmer scheme has a better performance of security than the multi user scheduling sch eme.

Abstract:Subband sensor delay lines (SDL) performance based on linearly constrained minim um variance (LCMV) adaptive wideband beamforming algorithm is analyzed. The outp ut signal to interference plus noise ratio (SINR) of subband LCMV algorithm is derived. The performance of fullband LCMV beamforming and subband LCMV beamfo rming is compared, including array response and output SINR with different numbe r of elements, input signal to noise ratio (SNR), and element spacing. Output S INR performance of subband LCMV beamforming with different parameters of subband filter banks is also analyzed. Simulation results verify that the subband LCMV algorithm improves its performace and reduces the element need at no cost of pe rformance.

Abstract:I n CR networks, a transmitting and receiving beamforming algorithm is proposed. I n tran smitting beamforming, a multi beam algorithm is proposed to send different sign als to different secondary users. In receiving beamforming, a low side lobe beamformin g a lgorithm is presented. To avoid the interference when primary and secondary users share the same spectrum, the problem of transmitting beamforming is transf orme d i nt o the one that can be solved by CVX toolbox. In receive beamforming, based on the method of array pattern optimization, a low side lobe level rec e iving beamforming algorithm is proposed in primary base station to suppress the wh ite noise an d unexpected interference. Extensive numerical results using simulated wireless channels in two dimensions are presented to verify the effectiveness of the algo rithm.

Abstract:The estimation accuracy of the mixing matrix is influenced by the sources sparsity in the underdetermined mixtures. Based on the analytical results of th e single and non single frequencies for source signals, through clustering the co l umn vectors composed by the ratios between the observation signal frequency amp litudes, a new method for the mixing matrix estimation is proposed when the sources are little sparse to each other. Considering the non stability brought by the par t ial convergence of the classical clustering algorithm, the genetic and simulated annealing clustering algorithm possessing the global convergence characteristic is u sed to prove the robustness of the clustering result. The experiment results s how that the proposed estimation method and the clustering algorithm can provide good estimation performance under different underdetermined conditions and different noises.

Abstract:The total le ast squares (TLS) approximation estimates a parameter matrix from a linear model considering errors in both the observation vector [WTHX]L[WTBZ] and the dat a matrix [WTHX]A.[WTBZ] The TLS theory is more rigorous than the standard least squ ares . If some columns of [WTHX]A[WTBZ] can be known exactly, the problem is defined as the mixe d least squares—TLS problem. An iterative algorithm is derived to solve the LS TL S problem by using the principle of indirect adjustment. Compared with the method based on singular value decomposition(SVD) QR decompositon, the iterative algorithm coincides with the SVD algorithm. The calculated example proves that the ite rative algorithm is valid and rational.

Abstract:Aiming at the stepped frequency ISAR imagi ng problems of multiple moving targets in the same antenna beam overlapped over the range, a new method of stepped frequency ISAR imaging of multiple tar gets is proposed, based on the chirp Fourier transform (CFT). On the basis of the m u lti target echoes model of stepped frequency radar , the velocities of each target can be estimated accurately by the CFT. Then the multiple target ec ho signals can be separated by combining CFT and clean idea, thus the ISAR imag ing is finished. The simulation results also prove the validity of the proposed method.

Abstract:A high capacity reversible watermarking algorithm for JPEG images is proposed. The al gorithm determines the embedding positions with the zero coefficients intervals of DCT coefficients in JPEG images. The embedding capacity is calculated based o n the zero values number in each interval. Moreover, it brings no additional information, e.g. the locatio n map. According to the zero coefficients intervals, watermark information can be extracted accurately, and the original image can be recovered losslessly. Exp erimental results show that the algorithm can obtain the watermarked images wit h high perceptual quality and high capacity, and the increment of image file siz e is smaller than other reversible watermarking schemes for JPEG images.

Abstract:The weighted sp atial smoothing (WSS) algorithm requires preliminary estimate direction of arri val o f coherent signals in the calculation of weight factors. Here a new improved weighted spat ial smoothing (IWSS) technique for the direction of arrival estimation of cohe re nt signals is proposed. It divides the array into several sub arrays with specia l s tructure, according to the different contribution to direction o f arrival estimation between the autocorrelation matrix and the cross correlation matrix . N ested spatial smoothing algorithm is applied to gain the weight matrix to decorre late and estimate the direction of arrival of coherent source signals. In c on trast to the WSS method, the higher decorrela tion performance and angel resolution are also obtained without a priori informati on and preliminary estimate angle value. Theoretical analysis and simulation r esul ts demonstrate the correctness and validity of the new algorithm.

Abstract:The current mainstream distance metric learning approaches that all need to so lve the positive semi definite programming problem (SDP) will lead to high compu tational complexity, and they are thus difficult to be applied to large scale dat asets well because of fully matrix characteristics decomposition operational in each loop iteration. A distance metric learning method based on fe ature grouping and eigenvalue optimization is proposed considering the above pro blems. Firstly, a feature grouping algorithm is introduced to segment i mage features into several groups according to the correlations between each dim ension of characteristics. Then, the SDP problem can be covered to eigenvalue optimization issue under some certain constraints. Therefore, only the maximum eigenvalues of matrix is needed in every loop iteration. Experiment results indi cate that the computational complexity and the learning time of metric matrix ar e reduced effectively. Besides, the classification results are improved compared with the traditional methods.

Abstract:The signal sparsity is often unknown, or even changed with time in spec trum sensing. Therefore, a variable step size sequential compressed de tection algorithm is proposed by combining the adaptive theory with the sequenti al compre ssed spectrum sensing technology. The functional relationship is established bet ween step size factor of the next needed measurement numbers and current distan c e from the likelihood ratio and the detection thresholds. In addition, the sh ortcoming of the fixed step size of the measurement increment in the existing se quential compressed sensing is overcomed in the proposed algorithm. Theoretical analysis and computer simulations are conducted by introducing the rules of step size adjustment with piecewise function and parabolic function, respecti vely. Simulations prove that, the proposed algorithm has the fast er detection speed, less measurements number and lower computational complexi ties, compared with the existing sequential compressed detection scheme.

Abstract:A credibility test method based on the featu res of the linear regression is proposed to evaluate the blind processing result s of binary phase shift keying (BPSK) signals such as the modulation recognitio n and parameter estimation. The reference signals are first constructed depending on the certain identified modulation results. Two features, the ordered re gression factor and sign of the segments regression, are defined to test the cr edibility of the processing results by analyzing the differences of linear reg ression characteristics of the correlation series modulus between the ori ginal and the ascending order. Simulation results show that the proposed method can be used to effectively verify the credibility for blind processing results o f BPSK signals both at low signal to noise ratio and the condition without a prior knowledge for signal parameters.

Abstract:To meet the needs of data compression and data fusion in structural health monit oring (SHM) based on wireless sensor networks (WSNs), a nove l method of multi sensor data fusion based on comp ressed sensing (CS) is proposed for wireless structural damage signal to realize dat a fusion and reconstruction for sparse signals. The damage signals of avi ation aluminum plate are measured and projected on to the linear measurement data through inner products with random Gaussian matrix. Measurement data are fused by the Bayesian algorithm. Finally, the damage signals can be reconstru cted by the CS method. The experiment results show that compared with the exist ing method, the proposed method can save the network bandwidth as well as energy, thanks to its good data fusion performance, anti noise property and better data co mpression effect.

Abstract:Automatic identification system （AIS） satellite receive r can receive signals from different cells with the same frequency band， and thes e signals form co channel interference (CCI).　Suppressing CCI is one of t h e most challenging issues of the AIS system. A simple serial receiver structure consisting of a direction of arrival(DOA) estimator, a beamformer and a demodulat or is proposed. In this structure, incident angles of all desired and int erference signals estimated by multiple signal classification (MUSIC) and Capon are given. Then minimum variance distortionless response (MVDR), minimum mean square error (MMSE) and matched filter (M F) beamforming algorithms are employed to make the steering vector align with the incident direction of the desired signal to suppress the undesired interference signals. Finally, a two bit differential demodulation algorithm is applied to d etect the useful signal. The simulation results show that the proposed structure, co mpared with the single receiving antenna, significantly suppresses C CI and notably improves the bit error rate (BER) performance of Gaussian filtered minimum shift keying (GMSK) by using mu ltiple antenna beamforming.

Abstract:The most a vailable direction of arrival (DOA) estimation algorithms require covariance mat rix estimation and eigendecomposition, or even its inversion, thus increasing the computational complexity. Here a novel sub aperture multiple sigal classification (MUSIC) algorithm f or DOA estimation based on fast Fourier transform (FFT) is proposed. Firstly, ea ch received data vector of uniform linear array (ULA) is portioned into four sub vectors. Then FFT is applied to each sub vector to achieve the coherent i n tegration. By utilizing the data corresponding to the peaks of coherent integrat ion in each sub vector, a reduced dimensional data vector is constructed for D OA estimation in terms of MUSIC. Since the full dimensional covariance matrix e stimation and eigendecomposition are avoided, the computational complexity is re latively low. Numerical examples are provided to verify the effectiveness and su periority of the proposed method.

Abstract:To suppress the co channel interference in downlink multi cell systems, a novel zero forcing (ZF) precoding method is proposed based on the principle of orthogonal projection in linear algebra. A joint interference channel matrix is constructed, and then the channel matrix of the target user is projected onto the null sp ace of the joint matrix. Hence, the precoding matrix of the target user lies in the null space of all interference users without interfering with others. Compar ed with the conventional ZF method, the projection based method performs better at low signal to noise ratio (SNR). However, it is inferior to the conventional one at high SNR. Therefore, an adaptive ZF method is proposed combining the traits of both the conventional method and the projection based one. The computational complexities of the proposed methods are analyzed and compared with the conventional one. Simulation results show that the proposed adaptive ZF method offers better performance than the conventional one over a whole SNR regio n, and the complexity of the proposed method is a little higher than that of the convent ional one.

Abstract:Density based spatial clustering of applications with noise （DBSCAN） has poor scalability on the data size, especially when the amount of data increases. Here an improved adaptive fast density based spatial clustering of applications with noise (F DBSCAN) algorithm is proposed, with no longer checks of the objects inside the neighborhood of core obj ects, but just the mark of them. Merging clusters is performed by determining whether th ere exist the marked objects in the neighborhood of core objects. Noisy objects are recognized by checking whether the neighborhood of border ones contains a core ones. The proposed algorithm can avoid the repeated checking of overlapping are a of the original DBSCAN without building the spatial index, thus improving its eff iciency substantially with time complexity approaching O(nlogn). The clustering quality of FDBSCAN is validated on both artificial and real datasets, and its efficiency is also validated on two real datasets from different industries. The empirical results suggest that FDBSCAN can achieve good clustering qualit y as well as better efficiency and scalability.

Abstract:The tonguelike curve variable step least mean square (VSLMS) algorithm is a clas sical LMS algorithm. The disadvantage of the algorithm is that its step formula can be disturbed easily by noise jamming, thus causing adaptive filter′s weigh ts fluctuate around the optimal weights. To solve the problem, the tonguelike c urve VSLMS algorithm is improved according to the characteristics that the corre lation of white Gaussian noise is weak. The improved tonguelike curve VSLMS algo rithm based on the correlation characteristic is presented. Tonguelike curve VSL MS algorithm′s ability of anti noise jamming is improved evidently. If two alg orithms choose the same parameters, the improved tonguelike curve VSLMS algorith m based on the correlation characteristic has the less steady state error than the tonguelike curve VSLMS algorithm. Under the condition that the two algorithm s are both convergent, the convergence rate of the improved tonguelike curve VSL MS algorithm is faster than that of the tonguelike curve VSLMS algorithm. The ab ove conclusions are testified through theoretical analysis and simulation.

Abstract:Since accurate channel state information plays an important role in single carri er frequency domain equalization (SC FDE), a frequency domain state space appr oach to the least squares (LS) estimation is proposed with the aid of Gaussian m essage passing. Based on the generalized distribution law, the likelihood functi on is divided into several blocks in Forney style factor graphs (FFG), and then a recursive algorithm is developed by defining an auxiliary matrix. Through exc hanging soft information on code bits, channel estimation can be performed joint ly with frequency equalization on the basis of turbo principle. Due to the speci al forms of the state transition matrix, an unbiased recursive estimators is obt ained by properly forcing a covariance matrix in the recursive algorithm to be d iagonal. The complexity of the proposed approach avoids matrix inversion and gro ws logarithmically with the length of the observation vector. Simulation results show that the new algorithm can achieve good performance with low computational complexity.

Abstract:Considering the pr oblem of cognitive radio intelligence optimization, a scheme of cognitive engine is proposed based on the glowworm swarm optimization with t distribution mu tat ion． The bionic principle of firefly algorithm is analyzed. The　 optimization mechanism is defined by mathematics and a mutation plan is also designed with t di stribution. Finally, the simulations are performed in the multi carrier system. The results show that the algorithm has higher converg e nce speed and larger objective function value than the basic GA algorithm. Furthermore, th e improved algorithm with t distribution mutation has the higher capability of the g loba l optimization than that of the basic GSO algorithm.