Opportunistic SpectrumAccess in Self-Similar Primary Traffic

The authors take a stochastic optimization approach to opportunity tracking and access in self-similar primary traffic. Based on a multiple time-scale hierarchical Markovian model, they formulate opportunity tracking and access in self-similar primary traffic as a Partially Observable Markov Decision Process. They 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.