Collaborative-Learning-Automata-Based Channel Assignment with Topology Preservation for Wireless Mesh Networks under QoS Constraints

Abstract

Wireless mesh networks have emerged as a new technology for providing cost-effective broadband Internet access to users living in different communities across the globe. However, due to changes in a network topology across different paths, it is a challenging task to handle heavy data traffic in a multichannel environment. To address this issue, we propose a new collaborative-learning-automata-based channel assignment with topology preservation in this paper. In the proposed scheme, learning automata (LA) are deployed at the nearest mesh routers to collaborate with each other for information sharing and data transmission while learning from an environment. For each performed action, the LA get a reward or a penalty from the environment. Based on the inputs from the environment, the LA update their action probability vector and then decide the next action. The performance of the proposed scheme is evaluated with respect to various metrics such as throughput, data delivery ratio, switching and buffering delays, effective transmission, and effective channel utilization.