Abstract
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.
Chapter PDF
Similar content being viewed by others
References
Ramjee, R., Nagarajan, R., Towsley, D.: On optimal call admission control in cellular networks. Wireless Networks Journal (WINET) 3(1), 29–41 (1997)
Bartolini, N.: Handoff and optimal channel assignment in wireless networks. Mobile Networks and Applications (MONET) 6(6), 511–524 (2001)
Bartolini, N., Chlamtac, I.: Call admission control in wireless multimedia networks. In: Proceedings of IEEE PIMRC (2002)
Pla, V., Casares-Giner, V.: Optimal admission control policies in multiservice cellular networks. In: Proceedings of the International Network Optimization Conference (INOC), pp. 466–471 (2003)
Soh, W.-S., Kim, H.S.: Dynamic bandwidth reservation in cellular networks using road topology based mobility prediction. In: Proceedings of IEEE INFOCOM (2004)
Zander, R., Karlsson, J.M.: Predictive and Adaptive Resource Reservation (PARR) for Cellular Networks. International Journal of Wireless Information Networks 11(3), 161–171 (2004)
Pla, V., Giménez-Guzmán, J.M., MartÃnez, J., Casares-Giner, V.: Optimal admission control using handover prediction in mobile cellular networks. In: Proceedings of the 2nd International Working Conference on Performance Modelling and Evaluation of Heterogeneous Networks (HET-NETs 2004) (2004)
Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Chichester (1994)
Sutton, R., Barto, A.G.: Reinforcement Learning. The MIT press, Cambridge (1998)
Das, T.K., Gosavi, A., Mahadevan, S., Marchalleck, N.: Solving semi-markov decision problems using average reward reinforcement learning. Management Science 45(4), 560–574 (1999)
Yener, C., Rose, A.: Genetic algorithms applied to cellular call admission: local policies. IEEE Transaction on Vehicular Technology 46(1), 72–79 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 IFIP International Federation for Information Processing
About this paper
Cite this paper
Gimenez-Guzman, J.M., Martinez-Bauset, J., Pla, V. (2005). Performance Bounds for Mobile Cellular Networks with Handover Prediction. In: Dalmau Royo, J., Hasegawa, G. (eds) Management of Multimedia Networks and Services. MMNS 2005. Lecture Notes in Computer Science, vol 3754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11572831_4
Download citation
DOI: https://doi.org/10.1007/11572831_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29641-6
Online ISBN: 978-3-540-32090-6
eBook Packages: Computer ScienceComputer Science (R0)