Dynamic channel allocation for mobile cellular traffic using reduced-state reinforcement learning

Authors:

19 October 2004

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

DOI:10.1109/wcnc.2004.1311428

Abstract

This paper presents a reduced-state reinforcement learning solution to the dynamic channel allocation problem in cellular telecommunication networks featuring mobile traffic and call handoffs. We examine the performance of table-based function representation used in conjunction with the on-policy reinforcement learning algorithm SARSA and show that the policy obtained using a reduced-state table-based technique provides an online dynamic channel allocation solution with superior performance in terms of new call and handoff blocking probability as well as significantly reduced memory requirements. The superior performance of the proposed state-reduced technique is illustrated in simulation examples.

Keywords

This publication has 3 references indexed in Scilit:

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2003
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