基于二次协方差矩阵的频谱感知算法
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摘要
协方差矩阵频谱感知算法不需要主用户先验信息,易于实现,但是,低信噪比时,协方差矩阵元素间差异变小,检测性能有待提高。为此,利用噪声的二次协方差矩阵方差大、主用户信号的二次协方差矩阵元素的相关性增强等特点,提出利用二次协方差矩阵方差和对角线元素的判决统计量,推导出判决门限。AWGN信道和Rayleigh信道下的仿真结果表明:新方法在低虚警概率条件下,检测性能有明显提升,同时抗噪声不确定度和抗频偏性能均有改进。
Covariance matrix spectrum sensing algorithm does not require primary user prior information. It can be easy to be realized. However, the difference between elements of the covariance matrix is not obvious at low SNR, and its detection performance needs to be improved. Therefore, by using large variance of the covariance of covariance(CoC) matrix of the noise, and the correlation of the elements of the CoC matrix of the main user signal is enhanced. Based on the variance of the CoC matrix and the diagonal elements, the decision statistic is proposed, and the judgment threshold is derived. The simulation experiments under AWGN channel and Rayleigh channel show that the performance of the new method is obviously improved under the low false alarm probability. At the same time, the anti-noise uncertainty and anti-frequency offset performance are improved.
Covariance matrix spectrum sensing algorithm does not require primary user prior information. It can be easy to be realized. However, the difference between elements of the covariance matrix is not obvious at low SNR, and its detection performance needs to be improved. Therefore, by using large variance of the covariance of covariance(CoC) matrix of the noise, and the correlation of the elements of the CoC matrix of the main user signal is enhanced. Based on the variance of the CoC matrix and the diagonal elements, the decision statistic is proposed, and the judgment threshold is derived. The simulation experiments under AWGN channel and Rayleigh channel show that the performance of the new method is obviously improved under the low false alarm probability. At the same time, the anti-noise uncertainty and anti-frequency offset performance are improved.
引文
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[4] SANSOY M,BUTTAR A S.Cyclostationary feature based detection using window method in SIMO cognitive radio system[C]//Computing,Communication and Automation (ICCCA),2016 International Conference on.IEEE,2016:1430-1434.
[5] 李晓燕.认知无线电中基于阵列天线和协方差矩阵的频谱感知算法研究[D].吉林:吉林大学,2014.
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[8] 贾琼,李兵兵.基于局部方差的MIMO频谱感知算法研究[J].电子与信息学报,2015,37(7):1525-1530.
[9] 毛翊君,赵知劲,沈雷,等.利用协方差矩阵信息的多天线频谱感知算法[J].信号处理,2017(7):927-933.
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[11] 郭应时,王畅,张亚岐.噪声方差对卡尔曼滤波结果影响分析[J].计算机工程与设计,2014,35(2):641-645.
[12] 宗序平.数理统计学及其应用[M].北京:机械工业出版社,2015:63-66.
[13] 马军.认知无线电网络频谱管理关键技术研究[D].成都:电子科技大学,2016.
[2] YUCEK T,ARSLAN H.A survey of spectrum sensing algorithms for cognitive radio applications[J].IEEE Communications Surveys & Tutorials,2009,11(1):116-130.
[3] SABERALI S A,BEAULIEU N C.Matched-filter detection of the presence of MPSK signals[C]//Information Theory and its Applications (ISITA),2014 International Symposium on.IEEE,2014:85-89.
[4] SANSOY M,BUTTAR A S.Cyclostationary feature based detection using window method in SIMO cognitive radio system[C]//Computing,Communication and Automation (ICCCA),2016 International Conference on.IEEE,2016:1430-1434.
[5] 李晓燕.认知无线电中基于阵列天线和协方差矩阵的频谱感知算法研究[D].吉林:吉林大学,2014.
[6] ZENG Y,KOH C L,LIANG Y C.Maximum eigenvalue detection:Theory and application[C]//Communications,2008.ICC’08.IEEE International Conference on.IEEE,2008:4160-4164.
[7] PENNA F,GARELLO R,SPIRITO M A.Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices[J].IEEE Communications Letters,2009,13(7):507-509.
[8] 贾琼,李兵兵.基于局部方差的MIMO频谱感知算法研究[J].电子与信息学报,2015,37(7):1525-1530.
[9] 毛翊君,赵知劲,沈雷,等.利用协方差矩阵信息的多天线频谱感知算法[J].信号处理,2017(7):927-933.
[10] CHARAN C,PANDEY R.Double threshold based spectrum sensing technique using sample covariance matrix for cognitive radio networks[C]//Communication Systems,Computing and IT Applications (CSCITA),2017 2nd International Conference on.IEEE,2017:150-153.
[11] 郭应时,王畅,张亚岐.噪声方差对卡尔曼滤波结果影响分析[J].计算机工程与设计,2014,35(2):641-645.
[12] 宗序平.数理统计学及其应用[M].北京:机械工业出版社,2015:63-66.
[13] 马军.认知无线电网络频谱管理关键技术研究[D].成都:电子科技大学,2016.