An afterstates reinforcement learning approach to optimize admission control in mobile cellular networks

Abstract. We deploy a novel Reinforcement Learning optimization te-chnique based on afterstates learning to determine the gain that can be achieved by incorporating movement prediction information in the ses-sion admission control process in mobile cellular networks. The novel te-chnique is able to find better solutions and with less dispersion. The gain is obtained by evaluating the performance of optimal policies achieved with and without the predictive information, while taking into account possible prediction errors. The prediction agent is able to determine the handover instants both stochastically and deterministically. Numerical results show significant performance gains when the predictive informa-tion is used in the admission process, and that higher gains are obtained when deterministic handover instants can be determined.