Abstract:

Traditional spectrum sensing based on compressed sensing assumes that the sparsity is known, in fact,it is unknown and time-varying. To solve the problem, a sparsity adaptive algorithm for wideband spectrum sensing was proposed. First, the distributed compressed sensing and restricted isometry property principle were adopted to estimate an initial sparsity value. Then the confidence coefficient was used to update the sparsity and the spectrum support set was obtained, which was occupied by a primary user. Simulation results show that the proposed method has better spectrum detection performance than the spectrum sensing method with a known sparsity, and losses spectrum availability a little in low SNR, and its complexity is small.

Key words: compressed sensing, spectrum sensing, sparsity estimation, restricted isometry property, confidence coefficient