Title | ||
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Entropy-based active learning for wireless scheduling with incomplete channel feedback. |
Abstract | ||
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Most of the opportunistic scheduling algorithms in literature assume that full wireless channel state information (CSI) is available for the scheduler. However, in practice obtaining full CSI may introduce a significant overhead. In this paper, we present a learning-based scheduling algorithm which operates with partial CSI under general wireless channel conditions. The proposed algorithm predicts the instantaneous channel rates by employing a Bayesian approach and using Gaussian process regression. It quantifies the uncertainty in the predictions by adopting an entropy measure from information theory and integrates the uncertainty to the decision-making process. It is analytically proven that the proposed algorithm achieves an ź fraction of the full rate region that can be achieved only when full CSI is available. Numerical analysis conducted for a CDMA based cellular network operating with high data rate (HDR) protocol, demonstrate that the full rate region can be achieved our proposed algorithm by probing less than 50% of all user channels. |
Year | DOI | Venue |
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2016 | 10.1016/j.comnet.2016.05.001 | Computer Networks |
Keywords | Field | DocType |
Opportunistic scheduling,Queue stability,Limited information,Machine learning | Information theory,Wireless,Computer science,Scheduling (computing),Computer network,Communication channel,Full Rate,Cellular network,Code division multiple access,Channel state information | Journal |
Volume | Issue | ISSN |
104 | C | 1389-1286 |
Citations | PageRank | References |
3 | 0.44 | 26 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mehmet Karaca | 1 | 73 | 10.01 |
Özgür Erçetin | 2 | 146 | 22.96 |
Tansu Alpcan | 3 | 1383 | 114.46 |