Maximizing System Throughput by Cooperative Sensing in Cognitive Radio Networks

Cognitive radio networks (CRNs) allow unlicensed users to opportunistically access the licensed spectrum without causing disruptive interference to the primary users (PUs). One of the main challenges in CRNs is the ability to detect PU transmissions. Recent works have suggested the use of secondary user (SU) cooperation over individual sensing to improve sensing accuracy. In this paper, we consider a CRN consisting of multiple PUs and SUs to study the problem of maximizing the total expected system throughput. First, we study the sensing decision problem for maximizing the system throughput subject to a constraint on the PU throughput, and we design a Bayesian decision rule-based algorithm. The problem is shown to be strongly NP-hard and solved via a greedy algorithm with time complexity O([(N⁵)/(log²[1/(1-ε)])]), where N is the total number of SUs. The algorithm achieves a throughput strictly greater than 1/2(1-ε) of the optimal solution and results in a small constraint violation that goes to zero with ε. We then investigate the more general problem with constraints on both PU throughput and the sensing time overhead, which limits the number of SUs that can participate in cooperative sensing. We illustrate the efficacy of the performance of our algorithms and provide sensitivity analysis via a numerical investigation.