Reinforcement Learning-Based Dynamic Guard Channel Scheme with Maximum Packing for Cellular Telecommunications Systems

This paper presents a distributed reinforcement learning solution to the problem of call admission control for cellular telecommunication networks in the presence of both voice traffic and self-similar data traffic, and user mobility. The developed call admission control architecture is designed to make use of only localised information, and therefore is suitable for implementation in a distributed manner. By way of computer simulations, the call admission control is shown to further improve the revenue raising capability and handoff blocking probability of the optimal maximum packing channel allocation scheme.