Abstract
This paper introduces novel reinforcement learning agent-based solutions to the problems of call admission control (CAC) and dynamic channel allocation (DCA) in multi-cellular telecommunications environments featuring multi-class traffic and intercell handoffs. Both agents providing the CAC and DCA functionality make use of an on-policy reinforcement learning technique known as SARSA and are designed to be implemented at the cellular level in a distributed manner. Furthermore, both are capable of on-line learning without any initial training period. Both of the reinforcement learning agents are examined via computer simulations and are shown to provide superior results in terms of call blocking probabilities and revenue raised under a variety of traffic conditions
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