Optimization of energy detection based cooperative spectrum sensing in cognitive radio networks

Abstract

Cooperation is necessitated for tackling the challenges caused by fading/shadowing effects, noise uncertainty and sensing time constraints in spectrum sensing of cognitive radio networks. This paper studies the optimization problem of energy detection based cooperative spectrum sensing (CSS), with the main focuses on the optimality of K out of N fusion strategy and cooperative-user number. The procedures for optimizing the fusion strategy under both the Neyman-Pearson (N-P) and Bayesian criteria are given, and the numerical results demonstrate that the optimal strategy outperforms others, with the maximum collective detection probability under N-P criterion, and the minimum detection risk under Bayesian criterion. Further, the optimal number of cooperative users is investigated, as a solution to the tradeoff between the cooperative spectrum sensing performance and the total sensing overhead. It is shown that the required sensing reliability and minimization of the sensing overhead can be guaranteed simultaneously, if only the local detection threshold and the fusion strategy are properly set.