Spectrum detection based on Bayesian inference

In a cognitive radio network, secondary users are required to have autonomous knowledge of the real state of the authorized user activity in order to achieve dynamic spectrum access successfully. Since the prior knowledge may be free and the received main signal may have been digitally modulated, so a Bayesian spectrum sensing detection method is proposed that uses Bayesian inference to implement better spectrum utilization. Through the derivation of the AWGN channel, the detector structure of the MPSK modulated main signal of the known order is derived, and then the suboptimal detector corresponding to it is given in the case of high and low signal-to-noise ratio (SNR) respectively. The simulation results show that the Bayesian detector has similar performance to the energy detector when the SNR is low, but the Bayesian detector has higher spectral efficiency when the SNR is high.