Academic Journal of Computing & Information Science, 2023, 6(7); doi: 10.25236/AJCIS.2023.060716.
Jiawei Luo1, Jialu Wang2, Bo Wen3, Yu Yan4, Zebin Ma1, Ruihan Chen1, Minhua Ye5, Xuewen Chen1, Yueyang Wu1, Di Ning6
1School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, 524088, China
2College of Applied Mathematics, Chengdu University of Information Technology, Chengdu, 610225, China
3School of Information and Communication Engineering, North University of China, Taiyuan, 030000, China
4College of Communication Engineering, Chengdu University of Information Technology, Chengdu, 610225, China
5College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang, 524088, China
6School of Economics, Guangdong Ocean University, Zhanjiang, 524088, China
The advancement of 5G networks has sparked widespread interest in effectively strategizing the placement of communication base stations within areas of weak network coverage. Addressing this pertinent topic, this paper aims to enhance service coverage and reduce the construction costs associated with base stations in such areas. Specifically, we conduct a comprehensive analysis by selecting base station sites within a given area comprising 2500×2500 points, taking into account both ideal and real-world conditions. To ensure practical applicability, we strive to develop an optimal base station site selection scheme for 5G construction enterprises. This is accomplished through the construction of a multi-objective planning model that considers multiple constraints. By building upon this foundation, we further refine and optimize the conditions of the multi-objective planning model. This step enables us to obtain a new multi-objective planning model that aligns with the real-life requirement of fan-shaped base station communication coverage. To effectively solve the listed multi-objective planning model and obtain an optimized station site plan, we employ an accelerated genetic algorithm based on real number coding. Furthermore, to provide visual representation and analysis of the base station solution, this paper utilizes Python and ArcGIS, facilitating the visualization of the results. Through this approach, we can effectively explore and demonstrate the efficacy of the proposed base station placement strategy.
Accelerated genetic algorithm based on real number encoding, Multi-objective planning, Mobile communication network site planning, ArcGIS
Jiawei Luo, Jialu Wang, Bo Wen, Yu Yan, Zebin Ma, Ruihan Chen, Minhua Ye, Xuewen Chen, Yueyang Wu, Di Ning. Accelerated genetic algorithm based on real number coding for solving mobile communication network site planning. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 124-130. https://doi.org/10.25236/AJCIS.2023.060716.
[1] Cen Zhongdi. Solution of multi-objective planning based on genetic algorithm [J]. Journal of Zhejiang Wanli College, 2001(02):6-8.
[2] Wang Xiaohua, Yang Na. Parameter optimization estimation model based on genetic algorithm [J]. Electronic World, 2012(24):118-119.
[3] Li Jiaxin. A study on multi-target microgrid optimization scheduling based on legacy algorithm [J]. China Equipment Engineering, 2022(03):137-139.
[4] Fan ZK, Ren QH, Zhang GW. Directional modulation based on multi-objective genetic simulated annealing algorithm [J]. Journal of Air Force Engineering University (Natural Science Edition), 2021, 22(02): 48-53.
[5] Yan Chun, Li Meixuan, Zhou Xiao. Application of improved genetic algorithm in function optimization [J]. Application Research of Computers, 2019, 36(10):2982-2985.
[6] Jin Juliang, Yang Xiaohua, Ding Jing. Real Coding Based Acceleration Genetic Algorithm [J]. Journal of Sichuan University (Engineering Science Edition),2000,(04):20-24.
[7] Xu Ting. Analysis of the problems of 5G base station planning and construction [J]. China New Communication, 2021, 23(02):33-34.