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Learning to be energy-efficient in cooperative networks

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Learning to be energy-efficient in cooperative networks. / Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo et al.
In: IEEE Communications Letters, Vol. 20, No. 12, 12.2016, p. 2518-2521.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tian, D, Zhou, J, Sheng, Z & Ni, Q 2016, 'Learning to be energy-efficient in cooperative networks', IEEE Communications Letters, vol. 20, no. 12, pp. 2518-2521. https://doi.org/10.1109/LCOMM.2016.2608820

APA

Tian, D., Zhou, J., Sheng, Z., & Ni, Q. (2016). Learning to be energy-efficient in cooperative networks. IEEE Communications Letters, 20(12), 2518-2521. https://doi.org/10.1109/LCOMM.2016.2608820

Vancouver

Tian D, Zhou J, Sheng Z, Ni Q. Learning to be energy-efficient in cooperative networks. IEEE Communications Letters. 2016 Dec;20(12):2518-2521. Epub 2016 Sept 13. doi: 10.1109/LCOMM.2016.2608820

Author

Tian, Daxin ; Zhou, Jianshan ; Sheng, Zhengguo et al. / Learning to be energy-efficient in cooperative networks. In: IEEE Communications Letters. 2016 ; Vol. 20, No. 12. pp. 2518-2521.

Bibtex

@article{ae3e450e8b594d22a075b1a22d0848ed,
title = "Learning to be energy-efficient in cooperative networks",
abstract = "Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes{\textquoteright} selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit-equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy-efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances.",
author = "Daxin Tian and Jianshan Zhou and Zhengguo Sheng and Qiang Ni",
note = "{\textcopyright}2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2016",
month = dec,
doi = "10.1109/LCOMM.2016.2608820",
language = "English",
volume = "20",
pages = "2518--2521",
journal = "IEEE Communications Letters",
issn = "1089-7798",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",

}

RIS

TY - JOUR

T1 - Learning to be energy-efficient in cooperative networks

AU - Tian, Daxin

AU - Zhou, Jianshan

AU - Sheng, Zhengguo

AU - Ni, Qiang

N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/12

Y1 - 2016/12

N2 - Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes’ selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit-equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy-efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances.

AB - Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes’ selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit-equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy-efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances.

U2 - 10.1109/LCOMM.2016.2608820

DO - 10.1109/LCOMM.2016.2608820

M3 - Journal article

VL - 20

SP - 2518

EP - 2521

JO - IEEE Communications Letters

JF - IEEE Communications Letters

SN - 1089-7798

IS - 12

ER -