Technical Note: \cal Q-Learning
Machine Learning
Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement learning
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
Adaptive Bandwidth Reservation and Admission Control in QoS-Sensitive Cellular Networks
IEEE Transactions on Parallel and Distributed Systems
Call Admissibility for Multirate Traffic in Wireless ATM Networks
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
A Practical User Mobility Prediction Algorithm for Supporting Adaptive QoS in Wireless Networks
ICON '99 Proceedings of the 7th IEEE International Conference on Networks
Capacity of multiservice WCDMA networks with variable GoS
Wireless Networks
Journal of Parallel and Distributed Computing - 19th International parallel and distributed processing symposium
A predictive bandwidth reservation scheme using mobile positioning and road topology information
IEEE/ACM Transactions on Networking (TON)
Predictive channel reservation for handoff prioritization in wireless cellular networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
General game learning using knowledge transfer
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A simple and scalable handoff prioritization scheme
Computer Communications
Expert Systems with Applications: An International Journal
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
Reinforcement Learning and Dynamic Programming Using Function Approximators
Reinforcement Learning and Dynamic Programming Using Function Approximators
Expert Systems with Applications: An International Journal
Improvement of handover prediction in mobile WiMAX by using two thresholds
Computer Networks: The International Journal of Computer and Telecommunications Networking
Scaling model-based average-reward reinforcement learning for product delivery
ECML'06 Proceedings of the 17th European conference on Machine Learning
Handoff prioritization and decision schemes in wireless cellular networks: a survey
IEEE Communications Surveys & Tutorials
QoS provisioning in cellular networks based on mobility prediction techniques
IEEE Communications Magazine
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Admission control in IEEE 802.11e wireless LANs
IEEE Network: The Magazine of Global Internetworking
A distributed mobility control scheme in LISP networks
Wireless Networks
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The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. Although the optimum anticipation time depends on system parameters, we find that its value changes very little when the system parameters vary within a reasonable range. We also find that, in terms of system performance, deploying prediction is always advantageous when compared to a system without prediction, even when the system parameters are estimated with poor precision.