A Novel Distributed Compressive Wideband Spectrum Sensing Method in Cognitive Radio Networks

Abstract

In this paper, we present an optimum weighted approach for wideband spectrum sensing. Distributed compressive sensing technology is exploited to obtain dramatic rate reductions while differential procedure is deduced to extremely enhance the detection sensitivity. The measurements are collected from each SU at a fusion center, where a C-out-of-J method is proposed to dramatically heighten the detection performance. SCSMP recovery algorithm is utilized to reconstruct the signals, which are then weighted by the estimated SNRs. Corroborating simulation results show that the raised algorithm can effectively reduce sampling rates at each SU, substantially raise the detection performance and saliently improve system robustness against noise.