A novel cooperative global spectrum sensing algorithm based on variational Bayesian inference

This paper proposed an approximate model for the global power spectral density (PSD) of primary users (PUs). Based on the proposed model, a novel cooperative spectrum sensing algorithm was proposed, and its overall flow was also built to obtain global information in the network of PUs. The global information includes locations, occupied frequency bands and transmit powers of the PUs. Then, an estimator of model coefficient vector was designed by utilizing the theory of Variational Bayesian Inference (VBI). Simulation results show that the proposed approximate model has good accuracy, and the corresponding estimation algorithm of model coefficient vector has good convergence and stability. Meanwhile, it is proved that the proposed algorithm has better Mean Square Error (MSE) performance since the sparsity of model coefficient vector is utilized.