A localized and distributed channel assignment framework for cognitive radio networks

Cognitive radio (CR) technology allows the observation of the network conditions to dynamically discover available and underutilized spectrum. A cognitive network is the framework that uses the CR to take advantage of underutilized spectrum. The availability of using multiple interfaces and channels with cognitive radio abilities in wireless devices is expected to alleviate the capacity limitations that exist in traditional single channel wireless mesh networks.; Although having multiple radio interfaces and available channels can generally increase the effective throughput, a problem arises as to what is the best strategy to dynamically assign available channels to multiple radio interfaces for maximizing effective network throughput by minimizing the interference. This thesis presents a distributed and localized interference-aware channel assignment framework for multi-radio wireless mesh networks in a cognitive network environment. The proposed framework uses a novel interference estimation method by utilizing distributed conflict graphs at each network interface to model the interference. Extensive simulation studies in 802.11 based multi-radio mesh networks have been performed. The results indicate that for both local and multi-hop traffic, the proposed protocol can facilitate a large increase in network throughput in comparison with a Common and Centralized Channel Assignment mechanisms that is used as a benchmark in the literature.