Intelligent base station management in greener traffic-aware cellular networks

Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Yves Louet, Honggang Zhang

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

    Abstract

    Traffic-aware cellular networks dynamically turn on/off some base stations (BSs) according to the predicted traffic variation pattern and thus are able to improve the energy efficiency while providing plenty of network capacity. In this paper, instead of depending on the predicted traffic knowledge, we formulate the traffic variations as a Markov chain and design an intelligent BS management scheme with the aid of reinforcement learning framework. Specifically, we propose a Transfer Actor-CriTic (TACT) algorithm, which leverages the concept of transfer learning and exploits the transferred learning expertise from historical periods or neighboring regions to obtain better energy saving performance.

    Original languageEnglish
    Title of host publication2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4673-5225-3
    DOIs
    Publication statusPublished - 17 Oct 2014
    MoE publication typeNot Eligible
    Event31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014 - Beijing, China
    Duration: 16 Aug 201423 Aug 2014

    Conference

    Conference31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014
    CountryChina
    CityBeijing
    Period16/08/1423/08/14

    Fingerprint

    Base stations
    Reinforcement learning
    Markov processes
    Energy efficiency
    Energy conservation

    Cite this

    Li, R., Zhao, Z., Chen, X., Louet, Y., & Zhang, H. (2014). Intelligent base station management in greener traffic-aware cellular networks. In 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS) [6929242] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/URSIGASS.2014.6929242
    Li, Rongpeng ; Zhao, Zhifeng ; Chen, Xianfu ; Louet, Yves ; Zhang, Honggang. / Intelligent base station management in greener traffic-aware cellular networks. 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS). IEEE Institute of Electrical and Electronic Engineers , 2014.
    @inproceedings{7f18e3506ea8479292c235f739d9a376,
    title = "Intelligent base station management in greener traffic-aware cellular networks",
    abstract = "Traffic-aware cellular networks dynamically turn on/off some base stations (BSs) according to the predicted traffic variation pattern and thus are able to improve the energy efficiency while providing plenty of network capacity. In this paper, instead of depending on the predicted traffic knowledge, we formulate the traffic variations as a Markov chain and design an intelligent BS management scheme with the aid of reinforcement learning framework. Specifically, we propose a Transfer Actor-CriTic (TACT) algorithm, which leverages the concept of transfer learning and exploits the transferred learning expertise from historical periods or neighboring regions to obtain better energy saving performance.",
    author = "Rongpeng Li and Zhifeng Zhao and Xianfu Chen and Yves Louet and Honggang Zhang",
    year = "2014",
    month = "10",
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    language = "English",
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    Li, R, Zhao, Z, Chen, X, Louet, Y & Zhang, H 2014, Intelligent base station management in greener traffic-aware cellular networks. in 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)., 6929242, IEEE Institute of Electrical and Electronic Engineers , 31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014, Beijing, China, 16/08/14. https://doi.org/10.1109/URSIGASS.2014.6929242

    Intelligent base station management in greener traffic-aware cellular networks. / Li, Rongpeng; Zhao, Zhifeng; Chen, Xianfu; Louet, Yves; Zhang, Honggang.

    2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS). IEEE Institute of Electrical and Electronic Engineers , 2014. 6929242.

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

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    N2 - Traffic-aware cellular networks dynamically turn on/off some base stations (BSs) according to the predicted traffic variation pattern and thus are able to improve the energy efficiency while providing plenty of network capacity. In this paper, instead of depending on the predicted traffic knowledge, we formulate the traffic variations as a Markov chain and design an intelligent BS management scheme with the aid of reinforcement learning framework. Specifically, we propose a Transfer Actor-CriTic (TACT) algorithm, which leverages the concept of transfer learning and exploits the transferred learning expertise from historical periods or neighboring regions to obtain better energy saving performance.

    AB - Traffic-aware cellular networks dynamically turn on/off some base stations (BSs) according to the predicted traffic variation pattern and thus are able to improve the energy efficiency while providing plenty of network capacity. In this paper, instead of depending on the predicted traffic knowledge, we formulate the traffic variations as a Markov chain and design an intelligent BS management scheme with the aid of reinforcement learning framework. Specifically, we propose a Transfer Actor-CriTic (TACT) algorithm, which leverages the concept of transfer learning and exploits the transferred learning expertise from historical periods or neighboring regions to obtain better energy saving performance.

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    Li R, Zhao Z, Chen X, Louet Y, Zhang H. Intelligent base station management in greener traffic-aware cellular networks. In 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS). IEEE Institute of Electrical and Electronic Engineers . 2014. 6929242 https://doi.org/10.1109/URSIGASS.2014.6929242