An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization

Wenchao Yang; Xu Bai; Shizeng Guo; Long Wang; Xuerong Luo; Mingjie Ji

doi:10.1007/978-981-15-8411-4_83

An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization

Wenchao Yang
, Xu Bai^*
, Shizeng Guo
, Long Wang
, Xuerong Luo
, Mingjie Ji

^*Corresponding author for this work

School of Electronics and Information Engineering

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

With the rapid development of 5G in recent years, the energy consumption in the information and communication industry is becoming serious day by day. The sleeping strategy of the base station (BS) is to consider the load situation and user distribution of each BS under the heterogeneous cellular network model and close the BS with low load. Meanwhile, some users of the BS with high load are assigned to the BS with low adjacent load, so as to achieve energy consumption balance. The simulation results show that the particle swarm optimization algorithm is superior to traditional distributed algorithm in energy consumption and energy saving efficiency, which can realize green communication, but the time it takes is a little longer.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems
Editors	Qilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Xiaoxia Li, Baoju Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	619-627
Number of pages	9
ISBN (Print)	9789811584107
DOIs	https://doi.org/10.1007/978-981-15-8411-4_83
State	Published - 2021
Event	9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 - Changbaishan, China Duration: 4 Jul 2020 → 5 Jul 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	654 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020
Country/Territory	China
City	Changbaishan
Period	4/07/20 → 5/07/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

5G
Energy consumption
Particle swarm optimization

Access to Document

10.1007/978-981-15-8411-4_83

Cite this

Yang, W., Bai, X., Guo, S., Wang, L., Luo, X., & Ji, M. (2021). An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization. In Q. Liang, W. Wang, X. Liu, Z. Na, X. Li, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems (pp. 619-627). (Lecture Notes in Electrical Engineering; Vol. 654 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-8411-4_83

Yang, Wenchao ; Bai, Xu ; Guo, Shizeng et al. / An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization. Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. editor / Qilian Liang ; Wei Wang ; Xin Liu ; Zhenyu Na ; Xiaoxia Li ; Baoju Zhang. Springer Science and Business Media Deutschland GmbH, 2021. pp. 619-627 (Lecture Notes in Electrical Engineering).

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title = "An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization",

abstract = "With the rapid development of 5G in recent years, the energy consumption in the information and communication industry is becoming serious day by day. The sleeping strategy of the base station (BS) is to consider the load situation and user distribution of each BS under the heterogeneous cellular network model and close the BS with low load. Meanwhile, some users of the BS with high load are assigned to the BS with low adjacent load, so as to achieve energy consumption balance. The simulation results show that the particle swarm optimization algorithm is superior to traditional distributed algorithm in energy consumption and energy saving efficiency, which can realize green communication, but the time it takes is a little longer.",

keywords = "5G, Energy consumption, Particle swarm optimization",

author = "Wenchao Yang and Xu Bai and Shizeng Guo and Long Wang and Xuerong Luo and Mingjie Ji",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 ; Conference date: 04-07-2020 Through 05-07-2020",

year = "2021",

doi = "10.1007/978-981-15-8411-4\_83",

language = "英语",

isbn = "9789811584107",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "619--627",

editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Xiaoxia Li and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems",

address = "德国",

}

Yang, W , Bai, X , Guo, S, Wang, L, Luo, X & Ji, M 2021, An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization. in Q Liang, W Wang, X Liu, Z Na, X Li & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol. 654 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 619-627, 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020, Changbaishan, China, 4/07/20. https://doi.org/10.1007/978-981-15-8411-4_83

An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization. / Yang, Wenchao ; Bai, Xu ; Guo, Shizeng et al.
Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Wei Wang; Xin Liu; Zhenyu Na; Xiaoxia Li; Baoju Zhang. Springer Science and Business Media Deutschland GmbH, 2021. p. 619-627 (Lecture Notes in Electrical Engineering; Vol. 654 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization

AU - Yang, Wenchao

AU - Bai, Xu

AU - Guo, Shizeng

AU - Wang, Long

AU - Luo, Xuerong

AU - Ji, Mingjie

PY - 2021

Y1 - 2021

N2 - With the rapid development of 5G in recent years, the energy consumption in the information and communication industry is becoming serious day by day. The sleeping strategy of the base station (BS) is to consider the load situation and user distribution of each BS under the heterogeneous cellular network model and close the BS with low load. Meanwhile, some users of the BS with high load are assigned to the BS with low adjacent load, so as to achieve energy consumption balance. The simulation results show that the particle swarm optimization algorithm is superior to traditional distributed algorithm in energy consumption and energy saving efficiency, which can realize green communication, but the time it takes is a little longer.

AB - With the rapid development of 5G in recent years, the energy consumption in the information and communication industry is becoming serious day by day. The sleeping strategy of the base station (BS) is to consider the load situation and user distribution of each BS under the heterogeneous cellular network model and close the BS with low load. Meanwhile, some users of the BS with high load are assigned to the BS with low adjacent load, so as to achieve energy consumption balance. The simulation results show that the particle swarm optimization algorithm is superior to traditional distributed algorithm in energy consumption and energy saving efficiency, which can realize green communication, but the time it takes is a little longer.

KW - 5G

KW - Energy consumption

KW - Particle swarm optimization

UR - https://www.scopus.com/pages/publications/85111354225

U2 - 10.1007/978-981-15-8411-4_83

DO - 10.1007/978-981-15-8411-4_83

M3 - 会议稿件

AN - SCOPUS:85111354225

SN - 9789811584107

T3 - Lecture Notes in Electrical Engineering

SP - 619

EP - 627

BT - Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Wang, Wei

A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Li, Xiaoxia

A2 - Zhang, Baoju

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020

Y2 - 4 July 2020 through 5 July 2020

ER -

Yang W , Bai X , Guo S, Wang L, Luo X, Ji M. An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization. In Liang Q, Wang W, Liu X, Na Z, Li X, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. Springer Science and Business Media Deutschland GmbH. 2021. p. 619-627. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-8411-4_83

An Adaptive Base Station Management Scheme Based on Particle Swarm Optimization

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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