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Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN

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Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN. / Wan Ariffin, Wan Nur Suryani Firuz; Zhang, Xinruo; Nakhai, Mohammad Reza.

IEEE International Conference on Communications (IEEE ICC 2016). IEEE, 2016.

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Harvard

Wan Ariffin, WNSF, Zhang, X & Nakhai, MR 2016, Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN. in IEEE International Conference on Communications (IEEE ICC 2016). IEEE. https://doi.org/10.1109/ICC.2016.7511448

APA

Wan Ariffin, W. N. S. F., Zhang, X., & Nakhai, M. R. (2016). Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN. In IEEE International Conference on Communications (IEEE ICC 2016) IEEE. https://doi.org/10.1109/ICC.2016.7511448

Vancouver

Wan Ariffin WNSF, Zhang X, Nakhai MR. Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN. In IEEE International Conference on Communications (IEEE ICC 2016). IEEE. 2016 https://doi.org/10.1109/ICC.2016.7511448

Author

Wan Ariffin, Wan Nur Suryani Firuz ; Zhang, Xinruo ; Nakhai, Mohammad Reza. / Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN. IEEE International Conference on Communications (IEEE ICC 2016). IEEE, 2016.

Bibtex Download

@inbook{1034fd3e4e754fa3956ee972abfdacf8,
title = "Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN",
abstract = "Without a proper observation of the energy demandof the receiving terminals, the retailer may be obliged to purchaseadditional energy from the real-time market and may take therisk of losing profit. This paper proposes two combinatorial multiarmedbandit (CMAB) strategies in green cloud radio accessnetwork (C-RAN) with simultaneous wireless information andpower transfer under the assumption that no initial knowledgeof forthcoming energy demand and renewable energy supplyare known to the central processor. The aim of the proposedstrategies is to find the set of optimal sizes of the energy packagesto be purchased from the day-ahead market by observing theinstantaneous energy demand and learning from the behaviour ofcooperative energy trading, so that the total cost of the retailer canbe minimized. Two novel iterative algorithms, namely, ForCMABenergy trading and RevCMAB energy trading are introduced tosearch for the optimal set of energy packages in ascending anddescending order of package sizes, respectively. Simulation resultsindicate that CMAB approach in our proposed strategies offersthe significant advantage in terms of reducing overall energy costof the retailer, as compared to other schemes without learningbasedoptimization.",
author = "{Wan Ariffin}, {Wan Nur Suryani Firuz} and Xinruo Zhang and Nakhai, {Mohammad Reza}",
year = "2016",
month = jul,
day = "14",
doi = "10.1109/ICC.2016.7511448",
language = "English",
booktitle = "IEEE International Conference on Communications (IEEE ICC 2016)",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Combinatorial Multi-armed Bandit Algorithms for Real-time Energy Trading in Green C-RAN

AU - Wan Ariffin, Wan Nur Suryani Firuz

AU - Zhang, Xinruo

AU - Nakhai, Mohammad Reza

PY - 2016/7/14

Y1 - 2016/7/14

N2 - Without a proper observation of the energy demandof the receiving terminals, the retailer may be obliged to purchaseadditional energy from the real-time market and may take therisk of losing profit. This paper proposes two combinatorial multiarmedbandit (CMAB) strategies in green cloud radio accessnetwork (C-RAN) with simultaneous wireless information andpower transfer under the assumption that no initial knowledgeof forthcoming energy demand and renewable energy supplyare known to the central processor. The aim of the proposedstrategies is to find the set of optimal sizes of the energy packagesto be purchased from the day-ahead market by observing theinstantaneous energy demand and learning from the behaviour ofcooperative energy trading, so that the total cost of the retailer canbe minimized. Two novel iterative algorithms, namely, ForCMABenergy trading and RevCMAB energy trading are introduced tosearch for the optimal set of energy packages in ascending anddescending order of package sizes, respectively. Simulation resultsindicate that CMAB approach in our proposed strategies offersthe significant advantage in terms of reducing overall energy costof the retailer, as compared to other schemes without learningbasedoptimization.

AB - Without a proper observation of the energy demandof the receiving terminals, the retailer may be obliged to purchaseadditional energy from the real-time market and may take therisk of losing profit. This paper proposes two combinatorial multiarmedbandit (CMAB) strategies in green cloud radio accessnetwork (C-RAN) with simultaneous wireless information andpower transfer under the assumption that no initial knowledgeof forthcoming energy demand and renewable energy supplyare known to the central processor. The aim of the proposedstrategies is to find the set of optimal sizes of the energy packagesto be purchased from the day-ahead market by observing theinstantaneous energy demand and learning from the behaviour ofcooperative energy trading, so that the total cost of the retailer canbe minimized. Two novel iterative algorithms, namely, ForCMABenergy trading and RevCMAB energy trading are introduced tosearch for the optimal set of energy packages in ascending anddescending order of package sizes, respectively. Simulation resultsindicate that CMAB approach in our proposed strategies offersthe significant advantage in terms of reducing overall energy costof the retailer, as compared to other schemes without learningbasedoptimization.

U2 - 10.1109/ICC.2016.7511448

DO - 10.1109/ICC.2016.7511448

M3 - Conference paper

BT - IEEE International Conference on Communications (IEEE ICC 2016)

PB - IEEE

ER -

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