Distributed Reduced-State SARSA Algorithm for Dynamic Channel Allocation in Cellular Networks Featuring Traffic Mobility

摘要

This paper presents a distributed reinforcement learning solution to the problem of dynamic channel allocation for cellular telecommunication networks in the presence of mobile call handoffs. The performance of various dynamic channel allocation schemes are compared via extensive computer simulations, and it is shown that a reduced-state SARSA reinforcement learning algorithm can achieve superior new call and handoff blocking probabilities. A new distributed reduced state SARSA algorithm is also developed which uses only local environment information readily available to the learning agent. By way of computer simulations, the distributed SARSA algorithm is shown to be capable of producing call blocking probabilities that are comparable to those obtained by the centralised learning agent.