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

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• 作者：Mehmet Karaca ; ^a ; ^{mehmet.karaca@eit.lth.se" class="auth_mail" title="E-mail the corresponding author} ; ^{mehmetkrc@sabanciuniv.edu" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Ozgur Ercetin^bAuthor Vitae ; Tansu Alpcan^cAuthor Vitae
• 关键词：Opportunistic scheduling ; Queue stability ; Limited information ; Machine learning
• 刊名：Computer Networks
• 出版年：2016
• 出版时间：20 July 2016
• 年：2016
• 卷：104
• 期：Complete
• 页码：43-54
• 全文大小：1025 K

文摘

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 ϵ 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.