Optimal admission control in multimedia mobile networks with handover prediction

Admission control is one of the key traffic management mechanisms that must be deployed in order to meet the strict requirements for dependability imposed on the services provided by modern wireless networks. We study the problem of optimizing admission control policies in mobile multimedia cellular networks when predictive information regarding the movement of mobile terminals is available. For the optimization process we deploy a novel reinforcement learning approach based on the concept of after states. The results obtained define theoretical limits for the gain that can be expected when using handover prediction, which cannot be established by deploying heuristic approaches. Numerical results show that the performance gain is a function of the anticipation time with which the admission controller knows the occurrence of handovers, and an optimal anticipation time exists. We also compare an optimal policy obtained deploying our approach with a previously proposed heuristic prediction scheme, showing that there is still room for technological innovation.