Multi-User Cooperation for Channel Selection in Cognitive Radio Networks: A Bayesian Approach

Abstract

In cognitive radio networks, the statistical duty cycles (traffic loads) of primary channels have a decisive effect on the throughput and link maintenance of secondary users (SUs). It is very difficult and time-consuming for the SUs to distinguish between primary channels in terms of duty cycles. This paper proposes a multi-user cooperation scheme where SUs share status information about the primary channels and the corresponding cooperation information processing scheme based on Bayesian inference to differentiate the primary channels in terms of statistical duty cycles within a short period of time. The cooperation information processing scheme helps the SUs quickly make accurate estimates of the statistical duty cycles of primary channels and theire uncertainties. Specifically, with the aid and information from its cooperating neighbors, a SU will continuously update the likelihood function and the priori distribution for each primary channel. Analytical and simulation results show that the proposed multi-user cooperation scheme, which is based on a Bayesian updating rule, can significantly save a SU's time in channel detection and selection, and therefore greatly improve its data transmission time and throughput.