Stochastic learning automata-based channel selection in cognitive radio/dynamic spectrum access for WiMAX networks

This paper proposes a cognitive radio-based dynamic bandwidth allocation scheme for secondary users in a cluster-based WiMAX network. It uses a learning automata-based algorithm to find the optimal transmission channel, while ensuring minimum channel loss and a considerably high signal-to-noise ratio, and concurrently minimizing costly channel switching activities when primary users request licensed channels. The objective is to coordinate efficient frequency utilization and frequency reusability in each of the clusters in the network and to make data transmission possible without depleting the spectrum. The proposed scheme subsumes unforeseen channel faults into the channel feedback and decides the optimal channel. The system converges asymptotically to an E-optimal solution. Copyright (c) 2014 John Wiley & Sons, Ltd.