Quantized Soft-Decision-Based Compressive Reporting Design for Underlay/Overlay Cooperative Cognitive Radio Networks

Abstract

Cooperative spectrum sensing (CSS) systems use underlay or overlay strategies to identify underused or unused bands to achieve higher spectrum utilization. In hybrid underlay and overlay systems, spectrum sensing results may be sparse and a general reporting strategy is required to take multiple spectrum usage status into account. In this paper, we propose a general quantized soft decision (QSD) based compressive sensing and reporting strategy for hybrid systems. Our objective is to provide a general framework to report more reliable sensing results to improve the sensing precision and reduce the collision probability with the low complexity. To this end, the soft sensing decisions are quantized to multiple levels, then they are compressed and encoded, while the characteristic information of secondary users (SUs) including the index of the SUs, the interference tolerance level etc, is transmitted over equivalent bit channels. Furthermore, considering that different users may have different quality of service requirements, we propose four methods to allow SUs to deliver local sensing results with different precisions. Simulations are performed over additive white Gaussian noise (AWGN) and Rayleigh fading channels. The results validate the theoretical analysis, and demonstrate that our scheme effectively improves the sensing decision precision and reduces the collision probability.