A novel compressed collaborative sensing scheme using LDPC technique

Abstract

Collaborative spectrum sensing (CSS) can significantly improve the performance of spectrum sensing based on the spatial diversity gain of different cognitive radio (CR). In wideband spectrum sensing scenario, since there might not be enough CRs in the network, or due to hardware limitations, each CR node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Based on the fact that the spectrum usage information the CR nodes collect has a common sparsity pattern, in this paper, we present a compressed collaborative wideband spectrum sensing scheme in cognitive radio networks. Under the hypothesis of joint sparsity, the CRs need to randomly detect a very small number of sub-channels according to a measurement matrix and send the results to a fusion center. To make the compressed sensing more effective, the scheme uses LDPC-like measurement matrix. Then the whole channel status can be recoverd by the fusion center through a low-complexity message passing algorithm. Numerical results shows that under a joint sparsity model, using the proposed distributed compressed sensing scheme, the CRs make a small number of measurements and get a high probability of detection.