A cooperative spectrum sensing algorithm based on Bayesian Compressed sensing

Compressed sensing (CS) has been applicably used in cognitive radio (CR) to sense wide-band spectrum, solving the wide-band sensing rate restriction problem. For a CR network which includes multiple nodes, it's considered that the received signals is high correlated. So in this paper, we focus on efficient compressed spectrum sensing reconstructions in CR network, proposing a probabilistic model called Cooperative Bayesian Compressed Spectrum Sensing (C-BCSS) algorithm. In the algorithm, node samples locally and a common sparse signal model is developed to the received signals where correlation exists. And then C-BCSS reconstruction is performed cooperatively at fusion center utilizing the information from all nodes. We compared C-BCSS with Multitask BCS and simulation results show that C-BCSS outperforms in condition of severe under-sampling and heavy noise.