Collaborative spectrum sensing for cognitive radio networks by using 1-bit compressed sensing

Abstract

As an essential task for enabling dynamic spectrum sharing, spectrum sensing in wideband cognitive radio (CR) networks becomes quite challenging due to high sampling pressure, huge data transmission overheads and serious channel fading. To overcome these challenges, this paper proposes a collaborative spectrum sensing scheme based on 1-bit compressed sensing (CS). Each SU performs local 1-bit CS and obtains its own estimated support set via signal reconstruction. To make use of joint sparsity, all the support sets are fused in the fusion center (FC) and the fused result is used as priori information to guide the next local signal reconstruction. The weighted binary iterative hard thresholding (BIHT) algorithm with support estimate vector is proposed to implement the second reconstruction. Simulation results show that our proposed scheme can achieve effective spectrum detection at low communication and computation load.