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Online learning approach for predictive real-time energy trading in cloud-rans

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number2308
Issue number7
Published1 Apr 2021

Bibliographical note

Funding Information: Funding: This research was funded by the Fundamental Research Grant Scheme, FRGS/1/2018/ TK10/UNIMAP/02/11, from the Ministry of Education Malaysia (MOE), Malaysia and Universiti Malaysia Perlis (UniMAP), Malaysia. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs.

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