Block spectrum sensing based on prior information in cognitive radio networks

Spectrum sensing is an important topic in cognitive radio networks. The traditional schemes suffer from high time consumption, hardware loss and computational complexity. To overcome the above shortcomings, compressed sensing is integrated into cognitive radio networks. In this paper, we propose two compressed spectrum sensing algorithms called Logit Weighted Block Orthogonal Matching Pursuit (LW-BOMP) and Logit Weighted Block Orthogonal Matching Pursuit with Joint Judgement (LW-BOMP-J), which exploit the block sparsity and prior information. The first algorithm integrates support probabilities into the matching pursuit procedure, and the second algorithm extends the first one to the scenario with inaccurate block support probabilities. The performance of the algorithms is simulated and compared with conventional algorithm. The results show that the proposed algorithms are more promising in reconstructing original spectrum and LW-BOMP-J is better than LW-BOMP under the inaccurate prior information condition.