基于支持向量机的高能效频谱感知算法研究
作者:
作者单位:

1.中国空间技术研究院通信与导航卫星总体部,北京 100094;2.国家航天局卫星通信系统创新中心, 北京 100094

作者简介:

李亚秋(1974-),女,研究员,副总设计师,研究方向:卫星通信,航天器总体设计
陈明章(1963-),男,研究员,总设计师,研究方向:卫星通信,航天器总体设计。

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基金项目:

国家自然科学基金(61775237)资助项目;航天背景预研课题(105090401)资助项目。


Energy-Efficient Spectrum Sensing Algorithm Based on Support Vector Machines
Author:
Affiliation:

1.Institute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing 100094, China;2.Innovation Center of Satellite Communication System, China National Space Administration,Beijing 100094, China

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    摘要:

    为保证卫星通信系统在频谱竞争和拥挤的复杂电磁环境下可靠通信,提高频谱检测性能,利用支持向量机算法将对未占用的频带的检测问题转化为一个二分类问题。通过能量向量减去中心向量和基向量构造用来表征信号的特征向量,对特征向量学习得到用于判断频谱状态的支持向量机模型,采用模拟退火算法训练搜索最佳的高斯核参数。仿真结果表明,所提出的算法与单阈值和双阈值频谱感知算法相比具有更优的检测准确性和鲁棒性,同时高检测率有助于提高系统的吞吐量和能效,为后续认知卫星通信系统的建设提供了支撑。

    Abstract:

    In order to improve the performance of the spectrum detection, and reliable communication in the spectrum congestion and competition complex electromagnetic environment of satellite system, the spectrum detection is converted to a binary classification problem by employing the support vector machine (SVM) algorithm. Specifically, the feature vector, which is used to characterize the signals, is obtained by removing the central and basis vectors from the energy vector and the SVM model for determining the spectrum status is then constructed. Moreover, the optimal parameter of the Gaussian kernel is determined by adopting the simulated annealing (SA) algorithm. Simulation results show that the proposed scheme can achieve better spectrum detection accuracy and increase the detection robustness as well as improve the system throughput and energy efficiency as compared to the existing single threshold and double-threshold base spectrum sensing schemes. The work conducted in this paper could support the construction and development of future cognitive satellite communications systems.

    图1 认知卫星系统模型Fig.1 Cognitive satellite system model
    图2 算法模型Fig.2 Algorithm model
    图3 不同算法的错误检测概率比较Fig.3 Error detection probability comparison of different algorithms
    图4 不同噪声不确定度下的错误检测概率比较Fig.4 Error detection probability comparison of algorithms with different noise uncertainty
    图5 不同带宽下错误检测概率比较Fig.5 Error detection probability comparison of algorithms with different bandwidth
    图6 不同算法能效结果比较Fig.6 Energy efficient comparison of algorithms
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李久超,王薇,刘枫,张千,李亚秋,陈明章.基于支持向量机的高能效频谱感知算法研究[J].数据采集与处理,2021,36(2):232-239

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  • 收稿日期:2021-01-21
  • 最后修改日期:2021-03-09
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  • 在线发布日期: 2021-04-15