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| Title | Mobile Communication Network Base Station Deployment Under 5G Technology: A Discussion On The Combination Of Genetic Algorithm And Machine Learning |
|---|---|
| ID_Doc | 37256 |
| Authors | Zhang M.; Wang Y.; Shi B. |
| Year | 2025 |
| Published | Lecture Notes in Networks and Systems, 1351 LNNS |
| DOI | http://dx.doi.org/10.1007/978-3-031-88287-6_34 |
| Abstract | This paper discusses the site optimization technology of mobile communication network, especially in the aspects of enhancing coverage and optimizing base station layout. With the advance of 5G technology, the complexity of network design has increased significantly due to the density of base station deployment and the reduction of the coverage of a single base station. The aim of this study is to solve the problem of improving the weak coverage area and optimizing the quality of network service by mathematical modeling and statistical analysis. The algorithm model we use is customized to the specific network environment, and genetic algorithm is used to optimize the layout and machine learning technology to adapt to the network configuration under dynamic conditions. By effectively enhancing coverage and minimizing cost impact, the model demonstrated significant improvements in both urban and rural deployments. Sensitivity analysis emphasizes the robustness of the model and the critical role of data quality and computational resources in achieving the best results. The research results provide scalable and efficient base station layout and configuration methods for continuous improvement of mobile network design, which can adapt to current and future technological advances. The paper concludes with strategic recommendations for network operators, suggesting continuous optimization of the model and exploring the application of the developed strategies to areas outside of traditional mobile communications, such as smart city infrastructure and emergency management systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. |
| Author Keywords | 5G Technology; Base Station Deployment; Genetic Algorithm; Machine Learning; Mobile Communication Network; Network Coverage |
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