Delay Analysis for Cognitive Radio Networks with Random Access: A Fluid Queue View

We consider a cognitive radio network where multiple secondary users (SUs) contend for spectrum usage, using random access, over available primary user (PU) channels. Our focus is on SUs´ queueing delay performance, for which a systematic understanding is lacking. We take a fluid queue approximation approach to study the steady-state delay performance of SUs, for cases with a single PU channel and multiple PU channels. Using stochastic fluid models, we represent the queue dynamics as Poisson driven stochastic differential equations, and characterize the moments of the SUs´ queue lengths accordingly. Since in practical systems, a secondary user would have no knowledge of other users´ activities, its contention probability has to be set based on local information. With this observation, we develop adaptive algorithms to find the optimal contention probability that minimizes the mean queue lengths. Moreover, we study the impact of multiple channels and multiple interfaces, on SUs´ delay performance. As expected, the use of multiple channels and/or multiple interfaces leads to significant delay reduction.