Distributed variational sparse Bayesian compressed sensing based on factor graphs - Cui-tao ZHU; Fan YANG; Han-xin WANG; Zhong-jie LI (Tongxin xuebao)
A distributed variational sparse Bayesian compressed spectrum sensing algorithm based on factor graph was proposed,which decomposed the global spectrum sensing problem into local problem based on factor and variation.Belief propagation was used for the statistical inference of the spectrum occupancy,to implement the “soft fusion”.The temporal and spatial correlation information providing two-dimensional redundancies was exchanged among cooperative cognitive users to improve the detection performance under low SNR.Meanwhile,the algorithm prunes the divergence of hyper-parameters and the corresponding basis functions for reducing the load of communication.The simulation results show that this method can effectively achieve performance of spectrum sensing under a low sampling rate and the low SNR.