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王子力,宋晓鸥,王晓蓉. 基于随机矩阵的特征值方差的频谱感知检测算法[J]. 科学技术与工程, 2019, 19(28): 179-183.
WANG Zi-LI,WANG Xiao-rong.Spectrum Sensing Detection Algorithm based on Eigenvalue Variance[J].Science Technology and Engineering,2019,19(28):179-183.
基于随机矩阵的特征值方差的频谱感知检测算法
Spectrum Sensing Detection Algorithm based on Eigenvalue Variance
投稿时间:2019-03-03  修订日期:2019-04-19
DOI:
中文关键词:  频谱感知 特征值检测 特征值方差
英文关键词:spectral sensing eigenvalue detection eigenvalue variance
基金项目:
        
作者单位
王子力 武警工程大学信息工程学院
宋晓鸥 武警工程大学信息工程学院
王晓蓉 武警工程大学信息工程学院
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中文摘要:
      传统的频谱感知能量检测易受噪声方差不确定性的影响,存在“信噪比墙”效应。频谱感知特征值检测跟能量检测一样,不需要信号任何先验信息,并且能在低信噪比下取得较好的检测性能。经典的特征值检测有最大最小特征值之比算法(MME),最大最小特征值之差算法(DMM)等。这些算法只利用特征值的一阶统计量,不能充分反映全部特征值的统计特征。本文利用特征值二阶统计量提出一种基于特征值方差的频谱感知算法,选取能反映特征值整体波动的方差当作观测统计量,并利用矩阵迹的性质推导出该算法的理论门限。仿真证明,当噪声方差不确定性等于0dB时,该算法的检测性始终优于MME算法。当噪声方差不确定性等于0.2dB时,ED检测概率急剧下降,而EV算法检测概率仅下降10%左右,并且当SNR大于-17dB时,EV算法的检测概率优于ED算法和MME算法。
英文摘要:
      Energy detection as a kind of the traditional spectrum sensing is susceptible to the uncertainty of noise variance and there is a “signal to noise ratio wall” effect. Eigenvalue detection of spectrum sensing, the same as energy detection, does not require any prior information of the signal and can achieve better detection performance at low SNR. The classical eigenvalue detection methods include maximum-minimum eigenvalue ratio algorithm (MME), maximum-minimum eigenvalue difference algorithm (DMM) and so on. But these algorithms only use the first-order statistic of the eigenvalues and do not adequately reflect the statistical characteristics of all eigenvalues. In this paper ,a spectrum sensing algorithm based on eigenvalue variance is proposed by using the second-order statistic of eigenvalues. The variance which reflects the overall fluctuation of eigenvalues is selected as the observation statistic and the theoretical threshold of the algorithm is derived by the properties of the matrix trace. The simulation proves that when the uncertainty of noise variance is equal to 0dB, the EV algorithm is always better than the MME algorithm. When uncertainty of the noise variance is equal to 0.2 dB, the probability of ED algorithm drops sharply, but the detection probability of EV algorithm only decreases about 10%. And when the SNR is over -17dB, the detection probability of the EV algorithm is more than the ED algorithm and the MME algorithm.
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