Entropy-based active learning for wireless scheduling with incomplete channel feedback

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Karaca, Mehmet and Erçetin, Özgür and Alpcan, Tansu (2016) Entropy-based active learning for wireless scheduling with incomplete channel feedback. Computer Networks, 104 . pp. 43-54. ISSN 1389-1286 (Print) 1872-7069 (Online)

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Official URL: http://dx.doi.org/10.1016/j.comnet.2016.05.001


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 epsilon 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.

Item Type:Article
Uncontrolled Keywords:Opportunistic scheduling; Queue stability; Limited information; Machine learning
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication
ID Code:29489
Deposited By:Özgür Erçetin
Deposited On:08 Aug 2016 15:01
Last Modified:22 May 2019 13:39

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