Reinforcement learning for dynamic channel allocation in mobile cellular systems

In cellular communication systems, an important problem is to allocate the communication resource (bandwidth) so as to maximize the service provided to a set of mobile callers whose demand for service changes randomly. This problem is formulated as a dynamic programming problem and we use a reinforcement learning (RL) method to find dynamic channel allocation policies that are better than previous heuristic solutions. The policies obtained perform well for a broad variety of call traffic patterns. The superior performance of the proposed technique in terms of empirical blocking probability is illustrated in simulation examples.