We address the multicast problem in cognitive radio networks, where secondary users exploit channels temporarily unused by primary users (i.e., spectrum opportunities). 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. By taking into account this dependency, we propose a low-complexity approximation algorithm with bounded performance guarantee for constructing the minimum-energy multicast tree, which transforms the multicast problem into a directed Steiner tree problem. We also demonstrate this dependency by studying the impact of the traffic load of the primary network on the minimum-energy multicast tree.

We address the multicast problem in cognitive radio networks, where secondary users exploit channels temporarily unused by primary users (i.e., spectrum opportunities). 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. By taking into account this dependency, we propose a lowcomplexity approximation algorithm with bounded performance guarantee for constructing the minimum-energy multicast tree, which transforms the multicast problem into a directed Steiner tree problem. We also demonstrate this dependency by studying the impact of the traffic load of the primary network on the minimum-energy multicast tree.

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We take a stochastic optimization approach to opportunity tracking and access in self-similar primary traffic. Based on a multiple timescale 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.