A Model Based RL Admission Control Algorithm for Next Generation Networks

In this paper we study the call admission control problem to optimize the network operators´ revenue guaranteeing the quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a Semi-Markov Decision Process, and we use a model based Reinforcement Learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learns it on-line. We will show how our policy provides better solution than a classic greedy algorithm.