Novel Cooperative Spectrum Sensing Based on Limiting Eigenvalue Distribution

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.