Afrl-rx-wp-tp-2009-4129 Dynamic Channel Allocation in Wireless Networks Using Learning Automata (preprint)

Single channel based wireless networks have limited bandwidth and throughput and the bandwidth utilization decreases due to congestion and interference from other sources. In order to increase the throughput, transmission in multiple channels is considered as an option. In this paper, we propose a distributed dynamic channel allocation scheme using adaptive learning automata for wireless networks whose nodes are equipped with single radio interfaces. The proposed schemes, Adaptive Pursuit RewardInaction, Adaptive Pursuit Reward-Penalty, and Adaptive Pursuit Reward-Only, run periodically on the nodes, and adaptively find the suitable channel allocation in order to attain a desired performance. A novel performance index, which takes into account the throughput and the energy consumption, is considered. The proposed learning scheme is adaptive in the sense of updating rule. The update value of the probabilities in the each step is a function of the error in the performance index. Comparing the three schemes in simulation environment, it was shown that the Adaptive Pursuit Reward-Only scheme guarantees updating the probability of the channel selection for all the links – even the links that their current channel allocations do not provide a satisfactory performance, hence reducing the frequent channel switching on the links that cannot achieve the desired performance on any of the channels.