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A Bandit Approach to Price-Aware Energy Management in Cellular Networks

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number7887725
Pages (from-to)1609-1612
Number of pages4
Issue number7
Early online date27 Mar 2017
Accepted/In press27 Mar 2017
E-pub ahead of print27 Mar 2017
Published1 Jul 2017


  • A Bandit Approach to_ZHANG_Publishedonline27March2017_GREEN AAM (non-CC)

    final_version.pdf, 357 KB, application/pdf

    Uploaded date:23 Jun 2017

    Version:Accepted author manuscript

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King's Authors


We introduce a reinforcement learning algorithm inspired by the combinatorial multi-armed bandit problem to minimize the time-averaged energy cost at individual base stations (BSs), powered by various energy markets and local renewable energy sources, over a finite-time horizon. The algorithm sustains traffic demands by enabling sparse beamforming to schedule dynamic user-to-BS allocation and proactive energy provisioning at BSs to make ahead-of-time price-aware energy management decisions. Simulation results indicate a superior performance of the proposed algorithm in reducing the overall energy cost, as compared with recently proposed cooperative energy management designs.

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