Compressed wideband spectrum sensing based on weighted average consensus

In cognitive radio (CR) networks with dynamic spectrum sharing, the first and most important task is spectrum sensing and detection of spectrum holes. On the basis that the wireless signal of primary user in cognitive radio network is inherently sparse in frequency domain, this paper develops a distributed compressed wideband spectrum sensing approach which combines compressed sensing technology and weighted average consensus algorithm. Spectrum sensing takes two stages. At sensing stage, compressed sensing is performed at each CR nodes to sample the received time domain wideband signal at practical complexity and cost, and then each CR nodes locally reconstruct the frequency domain signal. At fusion stage, to take different individual situation of each CR nodes into consideration, a weighted soft measurement combining scheme without a fusion center is adopted to combine the local spectrum sensing results of each CRs in a distributed way to get the final estimation of the frequency domain signal, using weighted average consensus algorithm. Simulation results show that spectrum sensing performance is enhanced with the using of both compressed sensing and weighted average consensus algorithm, and the performance of using weighted average consensus algorithm is better than the performance of using average consensus algorithm when in the same CR network.