On optimal call admission control in cellular networks
Wireless Networks
Handoff and optimal channel assignment in wireless networks
Mobile Networks and Applications - Dial m for mobility: discrete algorithms and methods for mobile computing and communication
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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We determine the gain that can be achieved by incorporating movement prediction information in the session admission control process in mobile cellular networks. 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. We evaluate the impact of predicting only incoming handovers, only outgoing or both types together. The prediction agent is able to determine the handover instants both stochastically and deterministically.Two different approaches to compute the optimal admission policy were studied: dynamic programming and reinforcement learning. Numerical results show significant performance gains when the predictive information is used in the admission process, and that higher gains are obtained when deterministic handover instants can be determined.