Opportunistic Spectrum Access in Self-Similar Primary Traffic

Abstract

We take a stochastic optimization 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 round-robin structure that obviates the need to know the channel parameters; thus it is robust to channel model mismatch and variations. Furthermore, the myopic policy achieves comparable performance as the optimal policy that requires exponential complexity and assumes full knowledge of the channel model.

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