特征值极限分布的改进合作频谱感知

Novel Cooperative Spectrum Sensing Based on Limiting Eigenvalue Distribution

本文采用随机矩阵理论,分析和研究了多认知用户接收信号采样协方差矩阵的最小特征值的极限分布,针对基于最大最小特征值之差的合作频谱感知算法,提出了新的门限判决方法。此算法能有效克服噪声不确定度的影响,且不需预先知道授权用户信号的先验知识和噪声方差。仿真结果表明,与以前的感知算法相比,本文算法有更低的判决门限,在低信噪比、小采样时,在达到设定虚警概率的前提下,该算法能够获得更好的感知性能。

In this paper,based on the difference between the maximum eigenvalue and the minimum eigenvalue （DMM） cooperative spectrum sensing algorithm,a new threshold decision rule is proposed via analyzing the limiting distribution of the minimum eigenvalue of the covariance matrix of the received signals from multiple cognitive users（CUs） by means of random matrix theory （RMT）.The proposed scheme could effectively overcome the noise uncertainty and need neither the prior knowledge of the signal transmitted from primary user（PU）,nor the noise power in advance.Simulation results show that the proposed algorithm has lower decision threshold for a target false alarm probability,and it can get better sensing performance compared with the previous sensing schemes with low SNR and small sampling.