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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7093 |
| Date | 01 May 2002 |
| Creators | Steinbach, Carl |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 41 p., 8457203 bytes, 989455 bytes, application/postscript, application/pdf |
| Relation | AITR-2002-007 |
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