Base Station Location Mechanism of Power Wireless Private Network Based on MVO Algorithm

doi:10.3969/j.issn.1006-2475.2024.03.017

Abstract

Abstract:
Abstract： Aiming at the problem of base station location in power wireless private network， a base station site mathematic model containing construction cost， coverage and overlapping coverage is put forward. Then， the optimal layout of base station is implemented based on improved multi-verse optimizer algorithm. Considering the precocious convergence problem of multi-verse optimizer algorithm， the travel distance rate in the algorithm is improved first， and then tabu search algorithm is introduced to effectively solve the local optimal problem. Finally， the economics and feasibility of the proposed method is verified by conducting comprehensive analysis of the optimized base station deployment scheme. The experimental results show that the designed algorithm has higher optimization performance and convergence rate， can save cost budget and reduce co-channel interference while improving coverage， and has a good theoretical guidance for the site planning of power wireless private network.

Key words: Key words： power wireless private network, base station location, multi-verse optimizer algorithm, tabu search algorithm

CLC Number:

TN929.5
TP18

XU Yu-jia1, ZHANG Hua-mei1, 2. Base Station Location Mechanism of Power Wireless Private Network Based on MVO Algorithm[J]. Computer and Modernization, 2024, 0(03): 105-109.

References

［1］ PEREIRA M B， CAVALCANTI F R P， MACIEL T F. Particle swarm optimization for base station placement［C］// 2014 International Telecommunications Symposium （ITS）. IEEE， 2014. DOI：10.1109/ITS.2014.6948033.
［2］ WANG Y H， XIANG L S， LIU X Y. Base station location optimization based on genetic algorithm in CAD system［C］// 3rd International Conference on Human Centered Computing （HCC）. Springer， 2017：208-214.
［3］朱思峰，刘芳，柴争义. 基于免疫计算的WCDMA网络基站选址优化［J］. 电子与信息学报， 2011，33（6）：1492-1495.
［4］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.
［5］ LI L L， MA B L， JIA Z H， et al. Base station locations optimization in LTE using artificial immune algorithm［C］// 2017 10th International Symposium on Computational Intelligence and Design （ISCID）. IEEE， 2017：165-168
［6］宋黎. 基于遗传算法的通信基站规划方法研究［D］. 大连：大连理工大学， 2019.
［7］ WANG Z C. Improved particle swarm communication algorithm for wireless communication network base station optimization application［C］// 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications （ICMNWC）. IEEE， 2022：1-5.
［8］唐丽晴，应忠于，罗云. 基于鲸鱼优化改进算法的基站选址［J］. 计算机与现代化， 2020（9）：100-105.
［9］刘娟，杨春花. 粒子群果蝇混合改进算法在基站选址优化问题中的应用［J］. 计算机与数字工程， 2021，49（7）：1341-1345.
［10］ ZHOU C L， CHEN Z J. A practical base station location optimization based on four networks integration［C］// 2021 International Conference on Computer Engineering and Artificial Intelligence （ICCEAI）. IEEE， 2021：470-475
［11］冯佳勇. 基于模拟退火—遗传混合算法的电力无线专网基站选址机制［D］. 北京：北京邮电大学， 2018.
［12］谢宏福.泛在电力物联网环境下电力无线专网优化与安全控制研究［D］. 南京：南京邮电大学， 2021.
［13］徐炜. 基于混合免疫算法的LTE基站选址优化研究［D］. 重庆：重庆邮电大学， 2018.
［14］ VATSH I， GUPTA V， BHATTACHARYYA B. Optimizing base station deployment for LTE using metaheuristic algorithms［C］// 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking （ViTECoN）. IEEE， 2019. DOI： 10.1109/VITEGoN. 2019.
8899758.
［15］马一丁，张多纳，焦展宇，等. 基于遗传算法的基站选址技术研究［J］. 电子技术与软件工程， 2022，241（23）：25-30.
［16］潘翀，刘鑫. 基于改进NSGA-2 算法的电力无线专网基站选址研究［J］. 电力信息与通信技术， 2020，18（3）：34-39.
［17］邢宁哲，李信，常海娇，等. 基于改进人工鱼群算法的电力无线专网基站规划［C］// 中国电力科学研究院.2016智能电网发展研讨会论文集. IEEE， 2016：415-420.
［18］张吉，赵夙，朱晓荣. 基于大数据挖掘的LTE网络重叠覆盖优化方法［J］. 南京邮电大学学报（自然科学版）， 2020，40（6）：92-99.
［19］ MIRJALILI S， MIRJALILI S M， HATAMLOU A. Multi-verse optimizer： A nature-inspired algorithm for global optimization［J］. Neural Computing & Applications， 2016，27（2）：495-513.
［20］ JUI J J， AHMAD M A， RASHID M I M. Modified multi-verse optimizer for solving numerical optimization problems［C］// 2020 IEEE International Conference on Automatic Control and Intelligent Systems （I2CACIS）. IEEE， 2020：81-86.
［21］郑聪，周海峰，郑东强，等. 基于改进多元宇宙算法的主动配电网故障定位方法研究［J］. 电力系统保护与控制， 2023，51（2）：169-179.
［22］龙干，黄媚，方力谦，等. 基于改进多元宇宙算法优化ELM的短期电力负荷预测［J］. 电力系统保护与控制， 2022，50（19）：99-106.
［23］ JAENGCHUEA S， LOHPETCH D. A hybrid genetic algorithm with local search and tabu search approaches for solving the post enrolment based course timetabling problem： Outperforming guided search genetic algorithm［C］// 2015 7th International Conference on Information Technology and Electrical Engineering （ICITEE）. IEEE， 2015：29-34
［24］何铸宁. 无线专网网络规划与性能分析［D］. 北京：北京邮电大学， 2016.

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