Intelligent base station management in greener traffic-aware cellular networks

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

doi:10.1109/URSIGASS.2014.6929242

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 proceeding › Conference article in proceedings › Scientific › peer-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 language	English
Title of host publication	2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)
Publisher	IEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)	978-1-4673-5225-3
DOIs	https://doi.org/10.1109/URSIGASS.2014.6929242
Publication status	Published - 17 Oct 2014
MoE publication type	Not Eligible
Event	31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014 - Beijing, China Duration: 16 Aug 2014 → 23 Aug 2014

Conference

Conference	31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014
Country/Territory	China
City	Beijing
Period	16/08/14 → 23/08/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/URSIGASS.2014.6929242

Cite this

@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 = oct,

day = "17",

doi = "10.1109/URSIGASS.2014.6929242",

language = "English",

booktitle = "2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014 ; Conference date: 16-08-2014 Through 23-08-2014",

}

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 et al.
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 proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Intelligent base station management in greener traffic-aware cellular networks

AU - Li, Rongpeng

AU - Zhao, Zhifeng

AU - Chen, Xianfu

AU - Louet, Yves

AU - Zhang, Honggang

PY - 2014/10/17

Y1 - 2014/10/17

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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DO - 10.1109/URSIGASS.2014.6929242

M3 - Conference article in proceedings

AN - SCOPUS:84919742786

BT - 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014

Y2 - 16 August 2014 through 23 August 2014

ER -

Intelligent base station management in greener traffic-aware cellular networks

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UN SDGs

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