Delay analysis for cognitive radio networks supporting heterogeneous traffic

Cognitive radio networking is emerging as a promising paradigm for future wireless networks. In this paper, the delay performance of cognitive radio networks supporting heterogeneous traffic is analyzed. In order to guarantee primary users' (PUs) licensed membership, packets from PUs are distinguished from secondary users (SUs) by employing an absolute priority scheme. Meanwhile, various delay requirements over the packets from SUs are fully considered. The packets from SUs are classified into either delay-sensitive packets or delay-insensitive packets. Moreover, a novel relative priority strategy is designed between these two types of traffic by proposing a "transmission window" strategy. The delay performance of both a single-PU scenario and a multiple-PU scenario is thoroughly investigated employing queueing theory. In the multiple-PU scenario, a dynamic and adaptive channel selection scheme based on learning automata is developed with the objective of reducing the average delay for all SU packets. Numerical experiments are conducted and the results demonstrate the delay performance with respect to varied transmission window sizes. The results in the multiple-PU scenario verify that the proposed learning automata channel selection scheme significantly improves the delay performance of SU packets.