A polynomial-time algorithm for optimizing channel selection in Cognitive Radio Networks

Abstract

Cognitive Radio Network (CRN) technology allows secondary users (SUs) to transmit data exploiting the wireless resources not utilized by licensed primary users (PUs). Channel exploration by SUs for finding transmission opportunities incurs non-negligible costs and is a key challenge in successful operation of CRN. In this paper, we investigate the joint effect of channel exploration order and stopping rule for channel selection with the goal of maximizing SUs' throughput. The main contribution of this paper is the near optimal exploration order and optimal stopping rule, paired with a new and more general characterization of PUs' traffic configurations in CRN. The proposed algorithm uses an analytical model that incorporates channel and PUs' activity history to make the best decision at each decision point, according to the current SUs' exploration results. The simulation study shows that the proposed algorithm significantly improves the SUs' throughput over the existing methods. The proposed optimal stopping algorithm runs in polynomial-time, which is another major contribution of this paper, since it is a significant complexity reduction from the typical exponential order of backward induction algorithms.