Femtocells sharing management using mobility prediction model

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.