A spectrum sensing algorithm based on random matrix theory in cognitive radio networks
Yigang Zhou, Fei Tian · 2010
Spectrum sensing is the precondition for opportunistic spectrum access. The quiet period is commonly used during the sensing period to avoid the interference from the cognitive radio networks. In this paper, we propose a new sensing algorithm which can avoid the usage of quiet period to guarantee the transmission performance of secondary users. The presence or absence of the primary signal is judged by checking the significance of the second largest eigenvalue of the sample covariance matrix based on random matrix theory. This method is especially suitable for the situation that the signal to noise ratio is not very low and there is no prior knowledge about the primary signal and transmission channel. Simulation results illustrate the performance of the proposed algorithm when detecting the primary signal without quiet period.