Using Reinforcement Learning for Call Admission Control in Cellular Environments featuring Self-Similar Traffic

This paper details reinforcement learning architectures that efficiently provide the functions of dynamic channel allocation (DCA) and call admission control (CAC) for cellular telecommunications environments featuring both voice traffic and self-similar data traffic. These solutions are able to be implemented in a distributed manner using only localised environment information and without the need for any off-line training period. The performance of these reinforcement learning solutions are thoroughly examined via computer simulations and are shown to produce superior results in terms of both revenue raised and handoff blocking probability.