Cellular Learning Automata-Based Channel Assignment Algorithms for Wireless Mobile Ad Hoc Networks
The wireless Mobile Ad hoc NETwork (MANET) architecture has received a lot of attention recently. This paper considers the access of multiple channels in a MANET with multi-hop communication behavior. The authors point out several interesting issues when using multiple channels. Their proposed algorithms enable hosts to utilize multiple channels by switching channels dynamically, thus increasing network throughput and decrease packet delay. In this paper, they first introduce the model of cellular learning automata in which learning automata are used to adjust the state transition probabilities of cellular automata.