Reinforcement learning for dynamic channel allocation in cellular telephone systems

Satinder Singh, Dimitri Bertsekas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

144 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996
PublisherNeural information processing systems foundation
Pages974-980
Number of pages7
ISBN (Print)0262100657, 9780262100656
StatePublished - 1997
Externally publishedYes
Event10th Annual Conference on Neural Information Processing Systems, NIPS 1996 - Denver, CO, United States
Duration: Dec 2 1996Dec 5 1996

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference10th Annual Conference on Neural Information Processing Systems, NIPS 1996
Country/TerritoryUnited States
CityDenver, CO
Period12/2/9612/5/96

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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