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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gjika, A.T. Levorato, M. Ortega, A. Mitra, U. |
| Copyright Year | 2012 |
| Description | Author affiliation: Dept. of Electrical Engineering, University of Southern California, Los Angeles, USA (Gjika, A.T.; Levorato, M.; Ortega, A.; Mitra, U.) |
| Abstract | Due to the complexity of wireless network operations, estimation of cost-to-go functions requires a large number of observations and is impractical in many real-world networks. In this paper, a novel framework for the online learning of cost-to-go functions using a local wavelet transform is presented. The proposed technique allows a considerable reduction in the number of observations needed for accurate estimation. The approach is based on the representation of the trajectory of the logical state of the network as a graph. The observed state trajectory (and thus cost trajectory) is projected onto a subset of the nodes to construct a small graph summarizing paths of the overall graph. Low-complexity local lifting transform operations, then, are used to recover the cost-to-go function on the whole graph. Numerical results for a wireless network with ∼ 1000 states show that the estimation error of the proposed technique is decreased by ∼ 50% in the early stages of learning with respect to standard Q-learning. |
| Starting Page | 1002 |
| Ending Page | 1009 |
| File Size | 447091 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467345378 |
| e-ISBN | 9781467345392 |
| DOI | 10.1109/Allerton.2012.6483328 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-01 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wireless networks Estimation Transforms Vectors Trajectory Approximation methods Optimization |
| Content Type | Text |
| Resource Type | Article |
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