An Adaptive Channel Sensing Approach Based on Sequential Order in Distributed Cognitive Radio Networks

Abstract : We design an efficient sensing order selection strategy for distributed Cognitive Radio Networks (CRNs), where multiple CRs sense the channels sequentially for spectrum opportunities according to a channel Latin Square. We are particularly interested in the case that CRs’ quantity is more than the available channels’, where traditional approaches will have high probabilities of collision. We first introduce a system model and an adaptive sensing threshold for available channels which is estimated according to the sensing probability of the specific sequential order. Then, we propose a channel sensing and access strategy that can adjust its sensing and access probabilities based on the crowded degree of sequential order. Last, we conduct extensive simulations to compare the performance of our approach with other typical ones. Simulation results show that the proposed scheme achieves an outstanding performance on channel utilization in the case of heavy channel workload.