A hierarchical prediction model for two nodes-based IP mobile networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Congestion games with resource reuse and applications in spectrum sharing
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
A new Markov-based mobility prediction algorithm for mobile networks
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Mobility Prediction Using Mobile User Profiles
MASCOTS '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
Model for sharing femto access
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Combining local and global profiles for mobility prediction in LTE femtocells
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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Bandwidth sharing paradigm constitutes an incentive solution for the serious capacity management problem faced by operators as femtocells owners are able to offer a QoS guaranteed network access to mobile users in their femtocell coverage. In this paper, we consider a technico-economic bandwidth sharing model based on a reinforcement learning algorithm. Because such a model does not allow the convergence of the learning algorithm, due to the small size of the femtocells, the mobile users velocity and, more importantly, the randomness of their arrivals, we propose to use a mobility prediction approach based on the analysis of movements history of the mobile users. Knowing the next visited cell in advance provides more time to mobile user to negotiate with the access provider and to generate synchronized resource reservation requests that maximize the gain of the access provider.