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A Reinforcement-Learning Approach to Power Management

We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad~hoc wireless networks. From this thesis we conclude that mid-level power management policies can outperform low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7093
Date01 May 2002
CreatorsSteinbach, Carl
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format41 p., 8457203 bytes, 989455 bytes, application/postscript, application/pdf
RelationAITR-2002-007

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