José Manuel Giménez-Guzmán

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We deploy a novel Reinforcement Learning optimization technique based on afterstates learning to determine the gain that can be achieved by incorporating movement prediction information in the session admission control process in mobile cellular networks. The novel technique is able to find better solutions and with less dispersion. The gain is obtained by(More)
We study the problem of optimizing admission control policies in mobile multimedia cellular networks when predictive information regarding movement is available and we evaluate the gains that can be achieved by making such predictive information available to the admission controller. We consider a general class of prediction agents which forecast the number(More)
Wireless technologies have rapidly evolved and are becoming ubiquitous. An increasing number of users attach to the Internet using these technologies; hence the performance of these wireless access links is a key point when considering the performance of the whole Internet. In this paper we present a measurement-based analysis of the performance of an IEEE(More)
In cellular networks, repeated attempts occur as result of user behavior but also as automatic retries of blocked requests. Both phenomena play an important role in the system performance and therefore should not be ignored in its analysis. On the other hand, an exact Markovian model analysis of such systems has proven to be infeasible and resorting to(More)
In this paper we study the impact of incorporating handover prediction information into the call admission control process in mobile cellular networks. The comparison is done between the performance of optimal policies obtained with and without the predictive information. The prediction agent classifies mobile users in the neighborhood of a cell into two(More)
We study the impact of incorporating handoff prediction information in the session admission control process in mobile cellular networks. We evaluate the performance of optimal policies obtained with and without the predictive information, while taking into account possible prediction errors. Two different approaches to compute the optimal admission policy(More)
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 averagereward reinforcement learning (RL) approach based on afterstates to the design of optimal admission control policies in mobile(More)
We are concerned with the analytic solution of multiserver retrial queues including the impatience phenomenon. As there are not closed-form solutions to these systems, approximate methods are required. We propose two different generalized truncated methods to effectively solve this type of systems. The methods proposed are based on the homogenization of the(More)
Today, link-state routing protocols that compute multiple shortest paths predominate in data center and campus networks, where routing is performed either in layer three or in layer two using link-state routing protocols. But current proposals based on link-state routing do not adapt well to real time tra c variations and become very complex when attempting(More)