Opportunistic spectrum access in self similar primary traffic

We take a stochastic control approach to opportunity tracking and access in self-similar primary traffic. Based on a multiple time scale hierarchical Markovian model, we formulate opportunity tracking and access in self-similar primary traffic as a Partially Observable Markov Decision Process. We show that for independent and stochastically identical channels under certain conditions, the myopic sensing policy has a simple and robust structure that obviates the need to know the channel parameters. Furthermore, the myopic policy achieves comparable performance as the optimal policy that requires exponential complexity and assumes full knowledge of the channel model.