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Reinforcement learning for dynamic channel allocation in mobile cellular systems

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2 Author(s)
Ranjan, R. ; Dept. of Electron. & & Commun., Birla Inst. of Technol., Jaipur ; Phophalia, A.

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.

Published in:

Recent Advances in Microwave Theory and Applications, 2008. MICROWAVE 2008. International Conference on

Date of Conference:

21-24 Nov. 2008