Rbm Based Cooperative Bayesian Compressive Spectrum Sensing With Adaptive Threshold

Cooperative spectrum sensing schemes can enable cognitive radio (CR) users to efficiently identify the unoccupied channels or spectrum holes, as well as overcome the impact of shadowing and fading. Considering the hardware limitation, compressive sensing (CS) is a solution scheme to alleviate the requirements on the receiver hardware, which can recover the wideband sparse signal sampled at sub-Nyquist rate. In this paper, Restricted Boltzmann Machine (RBM) based cooperative Bayesian compressive spectrum sensing with adaptive threshold (RC-ABCS) is proposed for block sparse wideband signal. In this scheme, we use the Bayesian compressive sensing (BCS) model to sense the wideband sparse signal and report the results to a fusion center. In this fusion center, a proposed iterative algorithm of Relevance Vector Machine (RVM) with adaptive threshold is used to increase the recovered accuracy of block sparse wideband signals. And then we employ RBM learning to achieve the fusion decision based on the recovery signals of the multi-CR users. The simulation results show that the proposed scheme can increase the detection accuracy, enhance the ability of anti-interference and improve the convergence rate.