A User Demand Driven Approach for 5G Base Station Location Selection

Huang Wenhui; Wang Jiahui; Zhou Liping; Yue Kun

doi:10.7544/issn1000-1239.202330621

Journal of Computer Research and Development > 2025 > 62(3): 672-681. > DOI: 10.7544/issn1000-1239.202330621 CSTR: 32373.14.issn1000-1239.202330621

Huang Wenhui, Wang Jiahui, Zhou Liping, Yue Kun. A User Demand Driven Approach for 5G Base Station Location Selection[J]. Journal of Computer Research and Development, 2025, 62(3): 672-681. DOI: 10.7544/issn1000-1239.202330621

Citation:

PDF (1401 KB)

A User Demand Driven Approach for 5G Base Station Location Selection

Huang Wenhui^{1, 2,},
Wang Jiahui^{1, 2, ,},
Zhou Liping³,
Yue Kun^{1, 2}

1.
School of Information Science and Engineering, Yunnan University, Kunming 650500
2.
Yunnan Key Laboratory of Intelligent Systems and Computing (Yunnan University), Kunming 650500
3.
Editorial Board of Journal of Yunnan University (Natural Science Edition), Kunming 650500

Funds: This work was supported by the Major Science and Technology Special Foundation of Yunnan Province (202202AD080001) and the Program of Yunnan Key Laboratory of Intelligent Systems and Computing (202405AV340009).

More Information

Author Bio:
Huang Wenhui: born in 1999. Master. His main research interest includes big data analysis

Wang Jiahui: born in 1996. PhD, assistant professor. Member of CCF. His main research interest includes knowledge engineering

Zhou Liping: born in 1979. Master, editor. Her main research interest includes data mining

Yue Kun: born in 1979. PhD, professor, PhD supervisor. Senior member of CCF. His current research interest includes knowledge engineering
Received Date: July 30, 2023
Revised Date: May 19, 2024
Accepted Date: May 29, 2024
Available Online: June 30, 2024

Graphical Abstract

Abstract

Abstract

With the continuous development and rapid popularization of 5G networks, the number of user devices and potential demand is increasing sharply. However, the high frequency of 5G signals leads to significant propagation losses. In order to achieve broader coverage of user devices, it is necessary to optimize existing 5G base station sites or guide the selection of new base station sites with low cost and high efficiency. The state-of-the-art methods for site selection mostly use heuristic algorithms to optimize the sites. However, the convergence time increases exponentially with the increase of the number of possible 5G base station sites, bringing many challenges for the site optimization. Therefore, we propose the method of selecting 5G base station sites based on user demand points to sufficiently consider the communications among users. Specifically, the planning area gridding method is proposed to reduce the time complexity of computation for user demand points covered by base stations. Then, the concept of separate degree among base stations is proposed and measured based on the number of user demand points covered by the base station. We give the objective function that satisfies the submodularity and the greedy algorithm to obtain the optimal scheme of base station site selection. Experimental results show that the proposed method outperforms the comparative algorithms on all evaluation metrics, and can effectively improve the coverage of 5G base station signals. In the same base station planning area, our proposed method achieves the maximum coverage rate with the minimum number of 5G base stations, thereby effectively reducing the construction cost of 5G base stations.
- 5G base station site,
- location selection,
- user demand,
- separate degree,
- submodularity,
- greedy algorithm

FullText(HTML)

References (26)

References

[1]	Pana V S, Babalola O P, Balyan V. 5G radio access networks: A survey[J]. Array, 2022, 14: 100170 doi: 10.1016/j.array.2022.100170
[2]	Shi Yuning, Tian Guiyin, Hao Qiancheng, et al. The applicability of macro and micro base stations for 5G base station construction[C]//Proc of the 2nd Int Conf on Control and Intelligent Robotics. New York: ACM, 2022: 567−572
[3]	孙健,张文胜,王承祥. 5G高频段信道测量与建模进展[J]. 电子学报,2017,45(5):1249−1260 doi: 10.3969/j.issn.0372-2112.2017.05.031 Sun Jian, Zhang Wensheng, Wang Chengxiang. Developments on channel measurement and models in 5G high frequency band[J]. Acta Electronica Sinica, 2017, 45(5): 1249−1260 (in Chinese) doi: 10.3969/j.issn.0372-2112.2017.05.031
[4]	Binshtok M, Brafman R I, Shimony S E, et al. Computing optimal subsets[C]//Proc of the 22nd AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2007: 1231−1236
[5]	Amine O M, Khireddine A. Base station placement optimisation using genetic algorithms approach[J]. International Journal of Computer Aided Engineering and Technology, 2019, 11(6): 635−652 doi: 10.1504/IJCAET.2019.102495
[6]	张英杰,毛赐平,俎云霄,等. 基于免疫算法的TD-SCDMA网络基站选址优化[J]. 通信学报,2014,35(5):44−48 doi: 10.3969/j.issn.1000-436x.2014.05.006 Zhang Yingjie, Mao Ciping, Zu Yunxiao, et al. Immune algorithm-based base station location optimization in the TD-SCDMA network[J]. Journal on Communications, 2014, 35(5): 44−48 (in Chinese) doi: 10.3969/j.issn.1000-436x.2014.05.006
[7]	朱思峰,刘芳,柴争义,等. 基于免疫计算的IEEE 802.16j网络基站及中继站选址优化[J]. 计算机研究与发展,2012,49(8):1649−1654 Zhu Sifeng, Liu Fang, Chai Zhengyi, et al. Immune-computing-based location planning of base station and relay station in IEEE 802.16j network[J]. Journal of Computer Research and Development, 2012, 49(8): 1649−1654 (in Chinese)
[8]	李岳衡,王莉,崔磊,等. 基于粒子群算法的分布式MIMO系统圆形小区天线位置优化研究[J]. 电子学报,2015,43(6):1144−1151 doi: 10.3969/j.issn.0372-2112.2015.06.016 Li Yueheng, Wang Li, Cui Lei, et al. Antenna port placement optimization of distributed MIMO system in circular cell based on particle swarm optimization[J]. Acta Electronica Sinica, 2015, 43(6): 1144−1151(in Chinese) doi: 10.3969/j.issn.0372-2112.2015.06.016
[9]	Geng Bin, Geng Jianyu, Liu Kai, et al. Research on base station site selection in power wireless private network based on improved NSGA-2 algorithm[C]//Proc of the Conf on Computers, Information Processing and Advanced Education. Piscataway, NJ: IEEE, 2022: 196−199
[10]	Wang Yu, Cong Gao, Song Guojie, et al. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks[C]//Proc of the 16th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2010: 1039−1048
[11]	Doerr B, Doerr C, Neumann A, et al. Optimization of chance-constrained submodular functions[C]//Proc of the 24th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2020: 1460−1467
[12]	Laitila J, Moilanen A. New performance guarantees for the greedy maximization of submodular set functions[J]. Optimization Letters, 2017, 11(4): 655−665 doi: 10.1007/s11590-016-1039-z
[13]	Liu Weiyi, Yue Kun, Li Jianyu, et al. Inferring range of information diffusion based on historical frequent items[J]. Data Mining & Knowledge Discovery, 2022, 36(1): 82−107
[14]	Chen Xuanhao, Zhao Yan, Liu Guanfeng, et al. Efficient similarity-aware influence maximization in geo-social network[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(10): 4767−4780 doi: 10.1109/TKDE.2020.3045783
[15]	Cai Taotao, Li Jianxin, Mian A, et al. Target-aware holistic influence maximization in spatial social networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(4): 1993−2007
[16]	吴安彪,袁野,乔百友,等. 大规模时序图影响力最大化的算法研究[J]. 计算机学报,2019,42(12):2647−2664 Wu Anbiao, Yuan Ye, Qiao Baiyou, et al, The influence maximization problem based on large-scale temporal graph[J]. Chinese Journal of Computers, 2019, 42(12): 2647−2664 (in Chinese)
[17]	Zhang Huizhen, Zhang Kun, Zhou Yuyang, et al. An immune algorithm for solving the optimization problem of locating the battery swapping stations[J]. Knowledge-Based Systems, 2022, 248: 108883 doi: 10.1016/j.knosys.2022.108883
[18]	Jordan J, Palanca J, Marti P, et al. Electric vehicle charging stations emplacement using genetic algorithms and agent-based simulation[J]. Expert Systems with Applications, 2022, 197: 116739 doi: 10.1016/j.eswa.2022.116739
[19]	狄卫民,王然. 考虑多级设施中断的供应链选址—库存决策模型及优化算法[J]. 计算机集成制造系统,2021,27(1):269−282 Di Weimin, Wang Ran. Supply chain location-inventory decision model and its optimization algorithm with multi-echelon facility disruptions[J]. Computer Integrated Manufacturing Systems, 2021, 27(1): 269−282 (in Chinese)
[20]	盛屹涛,郑晓璇. 基于DBSCAN聚类的基站选址与扇区角度规划研究[J]. 南通职业大学学报,2022,36(4):64−68 doi: 10.3969/j.issn.1008-5327.2022.04.013 Sheng Yitao, Zheng Xiaoxuan. Site selection of base station and planning of sector angle based on DBSCAN clustering[J]. Journal of Nantong Vocational University, 2022, 36(4): 64−68 (in Chinese) doi: 10.3969/j.issn.1008-5327.2022.04.013
[21]	Benmoussa K, Hamdadou D, Roukh Z E A. GIS-based multi-criteria decision-support system and machine learning for hospital site selection: Case study oran, Algeria[J]. International Journal of Software Science and Computational Intelligence, 2022, 14(1): 1−19
[22]	Lan Tian, Cheng Hao, Wang Yi, et al. Site selection via learning graph convolutional neural networks: A case study of Singapore[J]. Remote Sensing, 2022, 14(15): 3579 doi: 10.3390/rs14153579
[23]	Nemhauser G L, Wolsey L A, Fisher M L. An analysis of approximations for maximizing submodular set functions—I[J]. Mathematical Programming, 1978, 14(1): 265−294 doi: 10.1007/BF01588971
[24]	Elhamifar E. Sequential facility location: Approximate submodularity and greedy algorithm[C]//Proc of the 36th Int Conf on Machine Learning. New York: PMLR, 2019: 1784−1793
[25]	Cho E, Myers S A, Leskovec J. Friendship and mobility: User movement in location-based social networks[C]//Proc of the 17th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2011: 1082−1090
[26]	李笠,王万良,徐新黎,等. 基于网格排序的多目标粒子群优化算法[J]. 计算机研究与发展,2017,54(5):1012−1023 doi: 10.7544/issn1000-1239.2017.20160074 Li Li, Wang Wanliang, Xu Xinli, et al. Multi-objective particle swarm optimization based on grid ranking[J]. Journal of Computer Research and Development, 2017, 54(5): 1012−1023 (in Chinese) doi: 10.7544/issn1000-1239.2017.20160074

[1]	Deng Xiaoheng, Cao Dejuan, Pan Yan, Shen Hailan, Chen Zhigang. An Optimized Credit Distribution Model in Social Networks with Time-Delay Constraint[J]. Journal of Computer Research and Development, 2017, 54(2): 382-393. DOI: 10.7544/issn1000-1239.2017.20151118
[2]	Zhang Fenxiang, Chen Huahui, Qian Jiangbo, Dong Yihong. HSSM: A Hierarchical Method for Streaming Submodular Maximization[J]. Journal of Computer Research and Development, 2016, 53(8): 1792-1805. DOI: 10.7544/issn1000-1239.2016.20160140
[3]	Wang Min, Wang Lei, Feng Xiaobing, Cao Baoxiang. An Approximation Algorithm of Betweenness Centrality Based on Vertex Weighted[J]. Journal of Computer Research and Development, 2016, 53(7): 1631-1640. DOI: 10.7544/issn1000-1239.2016.20148355
[4]	Zhang Dakun, Song Guozhi, Lin Huazhou, Ren Shuxia. Double Improved Genetic Algorithm and Low Power Task Mapping in 3D Networks-on-Chip[J]. Journal of Computer Research and Development, 2016, 53(4): 921-931. DOI: 10.7544/issn1000-1239.2016.20150682
[5]	Pei Tingrui, Yang Shu, Li Zhetao, Xie Jingxiong. Detouring Matching Pursuit Algorithm in Compressed Sensing[J]. Journal of Computer Research and Development, 2014, 51(9): 2101-2107. DOI: 10.7544/issn1000-1239.2014.20131148
[6]	Chen Xiaojun, Shi Jinqiao, Xu Fei, Pu Yiguo, Guo Li. Algorithm of Optimal Security Hardening Measures Against Insider Threat[J]. Journal of Computer Research and Development, 2014, 51(7): 1565-1577.
[7]	Chen Hao and Wang Yitong. Threshold-Based Heuristic Algorithm for Influence Maximization[J]. Journal of Computer Research and Development, 2012, 49(10): 2181-2188.
[8]	Zhang Zhi and Shi Pengfei. An Efficient Greedy Algorithm for Schema Matching[J]. Journal of Computer Research and Development, 2007, 44(11): 1903-1911.
[9]	Zhang Chunxiang, Li Sheng, and Zhao Tiejun. Phrase Alignment Based on Head-Phrase Extending[J]. Journal of Computer Research and Development, 2006, 43(9): 1658-1665.
[10]	Yi Yeqing, Lin Yaping, Lin Mu, Li Xiaolong, Wang Lei. Blind Source Separation Based on Genetic Algorithm[J]. Journal of Computer Research and Development, 2006, 43(2): 244-252.