PU probability prediction based Bayesian compressive spectrum sensing

Bayesian Compressive Sensing (BCS) can effectively relax the requirement of hardware operational bandwidth and perfectly recover sparse wideband signal at sub-Nyquist rate in wideband spectrum sensing. However, one of the problem of BCS is the long recovery time caused by the high computational complexity. In this paper, a PU Probability Prediction based Bayesian Compressive Sensing algorithm (PBCS) is proposed, in which PU (Primary User) probability prediction results are utilized as an index to select basis functions in the fast RVM (Relevance Vector Machine) algorithm to decrease the iterative times and thus reduce the recovery time. In addition, the simulation results are illustrated that it needs less measurements and has enhanced robustness against noise.