Performance analysis of wireless opportunistic schedulers using stochastic Petri nets

In this paper, performance of wireless opportunistic schedulers in multiuser systems is studied under a dynamic data arrival setting. Different from the previous studies which mostly focus on the network stability and the worst case scenarios, we emphasize on the average performance of wireless opportunistic schedulers. We first develop a framework based on Markov queueing model and then analyze it by applying decomposition and iteration techniques in the stochastic Petri nets (SPN). Since the size of the state space in our analytical model is small, the proposed framework shows an improved efficiency in computational complexity. Based on the established analytical model, performance of both opportunistic and non-opportunistic schedulers are studied and compared in terms of average queue length, mean throughput, average delay and dropping probability. Analytical results demonstrate that the multiuser diversity effect as observed in the infinite backlog scenario is only valid in the heavy traffic regime. The performance of the opportunistic schedulers in the light traffic regime is worse than that of the non-opportunistic round-robin scheduler, and becomes worse especially with the increase of the number of users. Simulations are also performed to verify the accuracy of the analytical results.