Sparse Bayesian Hierarchical Prior Modeling Based Cooperative Spectrum Sensing in Wideband Cognitive Radio Networks

This letter proposes a new method for cooperative spectrum sensing by exploiting sparsity. The novel scheme uses the theory of Bayesian hierarchical prior modeling in the framework of sparse Bayesian learning. This model has sparsity-inducing penalization terms leading to sparser solutions compared with typically l₁ norm based ones. Based on the factor graph that represents the signal model of the hierarchical prior models, the variational message passing (VMP) algorithm is implemented to estimate the power spectral density (PSD) map.