Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 34, 29 April 2024
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Layout optimization scheme for coexistence of multiple coverage capabilities in base stations—Based on genetic algorithm
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Abstract
With the development of 5G technology, communication networks have become increasingly complex, leading to a continuous growth in the number of base stations. While this has resulted in faster speeds, lower latency, and greater capacity, it has also presented a pressing issue: weak coverage points, areas with weak signal radiation from base stations. To address this challenge, this paper proposes a layout optimization scheme for the coexistence of base stations with multiple coverage capabilities, based on a genetic algorithm. The scheme considers the rational deployment of various types of base stations (represented as macro base stations and micro base stations in the paper) to improve the situation of weak coverage points. The specific steps of the optimization layout scheme include initializing existing base station coverage, calculating distances to weak coverage points, selecting new base station locations, updating data, and analyzing calculations. The final output includes the coordinates and types of new base stations needed. Through model simulation, the proposed optimization layout scheme can consider cost, coverage effectiveness, and threshold constraints while accommodating various requirements. Additionally, the scheme is highly applicable and can be integrated with computer analysis software. Finally, a dialectical analysis of the model is conducted, recognizing the flexibility and practicality advantages of the layout optimization scheme based on genetic algorithms, while acknowledging potential limitations such as overly idealistic assumptions.
Keywords
Communication site optimization, Genetic algorithm, Macro base station, Micro base station
References
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2. Qu Cheng. Research on Wireless Communication Base Station Layout Planning in Urban and Rural Planning[J]. Wireless Interconnect Technology, 2022, 19(20): 17-19.
3. Luo Yumei, Chen Wen. Application of Genetic Algorithm in Base Station Planning of Mobile Communication Systems[J]. Journal of Kunming Metallurgical College, 2014, 30(05): 24-28.
4. Xie Qingxi. Research and Implementation of Base Station Siting Optimization Problem Based on Intelligent Optimization Algorithm[D]. Shandong Normal University, 2018.
5. Xi Yugeng, Chai Tianyou, Yun Weimin. Overview of Genetic Algorithm[J]. Control Theory and Applications, 1996, (06): 697-708.
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-369-2
- ISBN (Online)
- 978-1-83558-370-8
- Published Date
- 29 April 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/34/20241158
- Copyright
- 29 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited