An Adaptive Learning Automata Algorithm for Channel Selection in Cognitive Radio Network

Channel selection plays a critical role in cognitive radio networks. In this work, we apply the learning automata techniques to enable a cognitive radio to learn and make decision on channel selection from a set of available channels. The set of randomly available frequency channels is modeled as an unknown environment. As practical networks are usually non-stationary, we propose an adaptive algorithm that enables the cognitive radio to monitor changes in the radio environment and always select the optimal channel after a long run.