Reduced-state SARSA with channel reassignment for dynamic channel allocation in cellular mobile networks

This paper proposes a novel solution to the dynamic channel allocation problem in cellular telecommunication networks featuring user mobility and call handoffs. We investigate the performance of a number of reinforcement learning algorithms including Q-learning and SARSA, and show via simulations that a reduced-state version of SARSA incorporating a limited channel reassignment mechanism provides superior performance in terms of new call and handoff blocking probability and a significant reduction in memory requirements.