Eigenvalue-Based Cooperative Spectrum Sensing Algorithm

Abstract

Accurate spectrum sensing is the key technique of cognitive networks. According to characteristic of random matrix, a cooperative spectrum sensing algorithm is proposed, which is based the eigenvalue of the signals covariance matrix. Regarded the average eigenvalue of the signals received at the different nodes as the average noise power, the ratio of maximum eigenvalue to average eigenvalue is used to decide whether the primary signal is present. It reduces the sensing period and improves the performance of spectrum sensing. Simulation results show that the cooperative algorithm has about 2 dB margin over other algorithms in signal-to-noise ratio.