Robustness of optimal channel reservation using handover prediction in multiservice wireless networks
| Autor: | Martínez Bauset, Jorge, Giménez Guzmán, José Manuel, Pla, Vicent |
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| Rok vydání: | 2012 |
| Předmět: |
Optimization
Handover prediction Computer Networks and Communications Computer science Real-time computing Mobile terminal Multiservice wireless networks Access control Cellular network Reinforcement learning approach Hand over Admission controllers Robustness (computer science) Reinforcement learning Optimal channels Electrical and Electronic Engineering Wireless network business.industry Mobile multimedia INGENIERIA TELEMATICA Admission control Predictive information Handover Theoretical limits Admission control policies Optimum value Performance Gain Channel reservation Channel reservations business Forecasting Information Systems |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| ISSN: | 1572-8196 1022-0038 |
| DOI: | 10.1007/s11276-012-0423-6 |
| Popis: | 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. © Springer Science+Business Media, LLC 2012. The authors would like to thank the reviewers for their valuable comments that helped to improve the quality of the paper. This work has been supported by the Spanish Ministry of Education and Science and European Comission (30% PGE, 70% FEDER) under projects TIN2008-06739-C04-02 and TIN2010-21378-C02-02 and by Comunidad de Madrid through project S-2009/TIC-1468. |
| Databáze: | OpenAIRE |
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