| In the first part, 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 my-opic sensing policy has a simple round-robin structure that obviates the need to know the channel parameters, thus 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.;In the second part, which is chapter 6, we consider the multicast problem in cognitive radio networks, where the existence of a communication link between two secondary users depends not only on the transmission power of the secondary transmitter and the distance between these two users, but also on the occurrence of spectrum opportunities. This dependency on the occurrence of spectrum opportunities complicates the construction of an efficient multicast tree in cognitive radio networks. With this dependency taken into account, we propose a low-complexity approximation algorithm with bounded performance guarantee for constructing the minimum-energy multicast tree. We also demonstrate this dependency by studying the impact of the traffic load of the primary network on the minimum-energy multicast tree. |
