Spectrum sensing based on Bayesian generalised likelihood ratio for cognitive radio systems with multiple antennas

In this paper, the spectrum sensing problem is addressed via using multiple antennas in cognitive radio systems when the noise and the primary user signal are independent. The problem of optimal detection is considered, and an estimation framework is presented where the primary user signal is a complex zero-mean Gaussian distribution. This paper discusses the generalized likelihood ratio detector (GLRT) when the sample size is a finite number and derive the corresponding GLRT detector of the spectrum sensing problem in a multiantenna framework by utilizing the statistics of the received signal, the channel information and the prior information of the noise. The scenario involves the assumption that the noise variance is unknown while the channel matrix gain is known for the secondary user (SU). The simulation results show that the proposed Bayesian GLRT detector is optimal even under a limited number of samples and more importantly also clarify that the proposed detector outperforms other the state-of-the-art detectors.