Dynamic Channel Selection in Cognitive Radio WiFi Networks

Abstract

Increased wireless network intelligence and interference awareness enabled by cognitive radio can lead to improved spectrum utilization and network performance. This thesis proposes a novel Dynamic Channel Selection (DCS) algorithm based around a cognitive radio platform developed by Communications Research Centre Canada (CRC). The proposed algorithm leverages the underlying cognitive radio platform to continuously monitor and dynamically adjust the channels of the network based on identified WiFi interference sources, thus relieving the network operator of having to manually configure and update the channels across the network. In recent years, the widespread adoption and deployment of WiFi networks has led to congestion and interference issues as there are only three non-overlapping channels in the 2.4GHz band where the majority of WiFi networks operate today. The proposed algorithm can detect both intra-network and external interference allowing it to vary the networks channels thereby reducing congestion and increasing throughput. Three tailoring factors allow the algorithm to be customized for various network environments and reduce unnecessary channel changes. An extensive performance evaluation is presented with field experiments in both rural and urban environments as well as the network simulation software, ns-3. The results demonstrate significant improvement in terms of throughput, spectrum efficiency as well as robustness to variations in the interference. The main contribution of this work is a DCS algorithm that uses a weighted edge graph to autonomously monitor and adjust a WiFi networks channels based on a variety of interference sources. The algorithm outperforms the current state of the art DCS algorithm and is the first of its kind to be implemented in a large scale cognitive radio field deployment.