摘要
利用样本协方差矩阵几何平均特征值的对数分布特性,提出了一种新的频谱感知算法。该算法基于样本协方差矩阵最大最小特征值之差与几何平均特征值的比值,通过比较该比值与门限的大小来判断主用户是否占用分配频谱,不需要知道主用户信号和噪声的先验信息,得到了十分简单的判决门限表达闭式。仿真结果表明,在低信噪比、低协作用户数以及低样本点数的条件下,所提算法能获得更优的感知性能;并且所提算法的感知性能较为稳定,受样本中极端值和虚警概率的影响均较小。
A new sensing algorithm based on the ratio of the difference of maximum-minimum eigenvalues to geometric mean eigenvalue of the sampled covariance matrix is proposed. The algorithm uses the logarithmic distribution characteristics of geometric mean eigenvalue, through comparing the ratio and threshold value to determine whether the primary user occupies the distribution spectrum, and the prior knowledge of the primary signal and noise are not needed, but a simple closed-form expression of thresh-old is obtained. The simulation results show that the proposed algorithm can get better perceived performance under the conditions of low signal to noise ratio, few collaborative users and few samples. On the other hand, it has steady sensing performance, it will be less affected by either the extreme values or the false-alarm probability.
出处
《电子技术应用》
2018年第1期79-83,共5页
Application of Electronic Technique
关键词
频谱感知
样本协方差矩阵
几何平均特征值
感知性能
spectrum sensing
sample covariance matrix
geometric mean eigenvalue
perceived performance
