A Reliable Collaborative Spectrum Sensing Scheme Based on the ROCQ Reputation Model for Cognitive Radio Networks

Abstract

Linear combination-based collaborative spectrum sensing, in which a coefficient is used to weight each cognitive radio (CR) user's sensing result in the linear fusion process at the central controller, is an effective mechanism to solve the fading and hidden terminal problem in cognitive radio networks (CRNs). For a reliable scheme, it is important to distinguish whether the CR user is reliable or not. In this paper, we proposed a collaborative spectrum sensing scheme based on the ROCQ reputation management model for CRNs. In the proposed scheme, each CR user has a reputation degree used to calculate its coefficient in the linear fusion process, and the reputation degree is initialized and adjusted by the central controller according to each CR user's sensing result, sensing correctness and report consistency. Simulation results show that our proposed scheme alleviates the corrupted sensing problem resulting from the destructive channel conditions between the primary transmitter and CR users. Moreover, compared to the optimal linear fusion scheme, the detection performance of our proposed scheme is approximately same while it requires no instantaneous SNRs. And the convergence of the reputation degree adjusting method also performs well.