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A Bandit Approach to Price-Aware Energy Management in Cellular Networks
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.
A Bandit Approach to Price-Aware Energy Management in Cellular Networks
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.
A Bandit Approach to Price-Aware Energy Management in Cellular Networks
Zhang, Xinruo (author) / Nakhai, Mohammad Reza (author) / Wan Ariffin, Wan Nur Suryani Firuz (author)
2017-07-01
Zhang , X , Nakhai , M R & Wan Ariffin , W N S F 2017 , ' A Bandit Approach to Price-Aware Energy Management in Cellular Networks ' IEEE COMMUNICATIONS LETTERS , vol 21 , no. 7 , 7887725 , pp. 1609-1612 . DOI:10.1109/LCOMM.2017.2687872
Article (Journal)
Electronic Resource
English
DDC:
690
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