Dynamic channel allocation for mobile cellular traffic using reduced-state reinforcement learning

This paper presents a reduced-state reinforcement learning solution to the dynamic channel allocation problem in cellular telecommunication networks featuring mobile traffic and call handoffs. We examine the performance of table-based function representation used in conjunction with the on-policy reinforcement learning algorithm SARSA and show that the policy obtained using a reduced-state table-based technique provides an online dynamic channel allocation solution with superior performance in terms of new call and handoff blocking probability as well as significantly reduced memory requirements. The superior performance of the proposed state-reduced technique is illustrated in simulation examples.