Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems

In cellular telephone systems, an important problem is to dynami(cid:173) cally allocate the communication resource (channels) so as to max(cid:173) imize service in a stochastic caller environment. This problem is naturally 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 traf(cid:173) fic patterns. In cellular communication systems, an important problem is to allocate the com(cid:173) munication resource (bandwidth) so as to maximize the service provided to a set of mobile callers whose demand for service changes stochastically. A given geograph(cid:173) ical area is divided into mutually disjoint cells, and each cell serves the calls that are within its boundaries (see Figure 1a).