Distributed Collaborative Compressive Spectrum Sensing in Multihop Cognitive Radio Networks

Abstract

As a key task for the implementation of cognitive radio (CR) systems, spectrum sensing confronts several technical challenges in the wideband CR networks, such as high sampling rates, limited hardware resources and wireless fading channels. To overcome these challenges, a distributed collaborative compressive spectrum sensing algorithm is developed in this paper. Each CR performs local compressive sensing to scan the wideband spectrum at affordable data acquisition costs. To achieve spatial diversity against wireless fading, CRs collaborate via one-hop communications only, and percolate the exchanged information across the multi-hop network to reach global convergence on the support set. All CRs share the same support set in the local sparse signal reconstruction, and thus joint sparsity is exploited to achieve reliable spectrum detection. Simulation results show that our proposed algorithm achieves effective spectrum detection at sub-Nyquist sampling rates, and has near-optimal detection performance in the absence of a fusion center.