Genetic Algorithm for Base Station ON/OFF Optimization with Fast Coverage Estimation and Probability Scaling for Green Communications

被引：0

作者：

Ren, Yebing ^{[1
,2
]}

Liu, Wei ^{[1
]}

Dong, Jiangbo ^{[1
]}

Wang, Haobin ^{[2
,3
]}

Liu, Yaxi ^{[3
]}

Wei, Huangfu ^{[2
,3
]}

机构：

[1] China Mobile Grp Design Inst Co Ltd, Beijing 100083, Peoples R China

[2] Beijing Engn & Technol Ctr, Convergence Networks & Ubiquitous Serv, Beijing 100083, Peoples R China

[3] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 10083, Peoples R China

来源：

SIGNAL AND INFORMATION PROCESSING, NETWORKING AND COMPUTERS (ICSINC) | 2019年 / 550卷

关键词：

Green communications; Base station ON/OFF strategy; Genetic algorithm; Probability; CELLULAR NETWORKS;

D O I：

10.1007/978-981-13-7123-3_10

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Minimizing the power consumption while maximizing the quality of service has become a mainstream problem in green communications. The existing approaches of network configurations often ignore the computation complexity caused by the large number of user terminals. Our contributions mainly lie in two folds. We formulate an optimization problem to maximize the user terminal coverage ratio with a given number of activated base stations. We propose a novel genetic algorithm to optimize the ON/OFF status of base stations with fast coverage estimation, in which the scaling and selection operators are carefully designed to take the probability distribution of the estimated coverage ratio into account. Experiments have been conducted to prove the proposed algorithm for the network configuration for green communication.

引用

页码：78 / 88

页数：11

共 15 条

[1] Cooperating Base Station Location Optimization Using Genetic Algorithm
Meng, Haijun
Long, Fei
Guo, Lu
Xiao, Yingqun
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 4820 - 4824
[2] Optimization of 5G base station coverage based on self-adaptive mutation genetic algorithm
Li, Jianpo
Pang, Jinjian
Fan, Xiaojuan
COMPUTER COMMUNICATIONS, 2024, 225 : 83 - 95
[3] Base station design for sector coverage using a genetic algorithm with the method of moments
Webb, DB
IEEE ANTENNAS AND PROPAGATION SOCIETY SYMPOSIUM, VOLS 1-4 2004, DIGEST, 2004, : 4396 - 4399
[4] User-Number Threshold-Based Base Station On/Off Control for Maximizing Coverage Probability
Noh, Jung-Hoon
Lee, Byungju
Oh, Seong-Jun
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (03) : 3214 - 3228
[5] Base Station Location Optimization Based on Genetic Algorithm in CAD System
Wang, Yanhua
Xiang, Laisheng
Liu, Xiyu
HUMAN CENTERED COMPUTING, HCC 2017, 2018, 10745 : 208 - 214
[6] Genetic Algorithm enabled Particle Swarm Optimization for Aerial Base Station Deployment
Zhang, Bo
Song, Jinpeng
Liu, Zhi
Yang, Kunhao
2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL), 2021,
[7] Base station deployment with capacity and coverage in WCDMA systems using genetic algorithm at different height
Wang, Chen-Shu
Chen, Yi-Dung
2012 SIXTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING (ICGEC), 2012, : 546 - 549
[8] A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers
Deng, Zhongliang
Mo, Jun
Jia, Buyun
Bian, Xinmei
ELECTRONICS, 2018, 7 (12):
[9] Joint Successive Base Station Switch Off and User Subcarrier Allocation Optimization for Green Multicarrier based Cellular Networks
Davarpanah, Danial
Zamani, Mohammadreza
Eslami, Mohsen
Niknam, Taher
2015 23RD IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2015, : 504 - 507
[10] Fast optimization of sparse antenna array using numerical Green's function and genetic algorithm
Raji, Mordecai F.
Zhao, Huapeng
Monday, Happy N.
INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 2020, 33 (04)

← 1 2 →