Bayesian Detector based Superior Selective Reporting Mechanism for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cognitive radio network(CRN) coupled with spectrum sensing technology enables unlicensed secondary users (SUs) to opportunistically access the unused licensed spectrum of primary users (PUs). Cooperative Spectrum Sensing (CSS) significantly improves the detection probability of primary user transmission. Nevertheless, current CSS techniques render shortcomings including energy consumption and overhead in sensing phase. Overheads are consequence of multiple cooperative SUs reporting their decision to the fusion center. In this paper, we propose Bayesian Detector based Superior Selective Reporting Cooperative Sensing(BD-SSRCS) scheme. Superior Selective Reporting (SSR) scheme, competently reduces reporting overhead and mitigates interference to PUs. Bayesian based sensing technique for local sensing improves detection performance, spectrum utilization and secondary user throughput. Our analysis and simulation results manifest the outcome of presented work in terms of higher detection probability, lower miss detection rate and lesser detection overhead, as opposed to the traditional cooperative sensing methods. Moreover, miss detection probability and sensing time can be reduced by ideally choosing sensing time allocation factor. (C) 2016 The Authors. Published by Elsevier B.V.