Spectrum Allocation Algorithms for Cognitive Radio Mesh Networks

Abstract

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use a given licensed spectrum band as long as there are no primary, licensed, users occupying this band in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. Heterogeneity in channel availability forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take this heterogeneity property and its effect on the network performance into consideration. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by the heterogeneity in channel availability. We proposed a