Open AccessProceedings Article
Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems
Satinder Singh,Dimitri P. Bertsekas +1 more
- Vol. 9, pp 974-980
TLDR
This work uses a reinforcement learning method to find dynamic channel allocation policies that are better than previous heuristic solutions and results are presented on a large cellular system with approximately 4949 states.Abstract:
In cellular telephone systems, an important problem is to dynamically allocate the communication resource (channels) so as to maximize 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 traffic patterns. We present results on a large cellular system with approximately 4949 states.read more
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