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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tekin, C. Mingyan Liu |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, 48109-2122, USA (Tekin, C.; Mingyan Liu) |
| Abstract | In this paper we develop an adaptive learning algorithm which is approximately optimal for an opportunistic spectrum access (OSA) problem with polynomial complexity. In this OSA problem each channel is modeled as a two state discrete time Markov chain with a bad state which yields no reward and a good state which yields reward. This is known as the Gilbert-Elliot channel model and represents variations in the channel condition due to fading, primary user activity, etc. There is a user who can transmit on one channel at a time, and whose goal is to maximize its throughput. Without knowing the transition probabilities and only observing the state of the channel currently selected, the user faces a partially observed Markov decision problem (POMDP) with unknown transition structure. In general, learning the optimal policy in this setting is intractable. We propose a computationally efficient learning algorithm which is approximately optimal for the infinite horizon average reward criterion. |
| Starting Page | 1548 |
| Ending Page | 1556 |
| File Size | 536159 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781467307734 |
| ISSN | 0743166X |
| e-ISBN | 9781467307758 |
| DOI | 10.1109/INFCOM.2012.6195522 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-03-25 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Markov processes Approximation algorithms Indexes Complexity theory Optimized production technology Probability Polynomials restless bandits Approximate optimality online learning opportunistic spectrum access |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Electrical and Electronic Engineering |
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