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Base Station Planning Based on Region Division and Mean Shift Clustering

Author

Listed:
  • Jian Chen

    (School of Mechanical Engineering, Yangzhou University, Huayang West Road 196, Yangzhou 225127, China)

  • Yongkun Shi

    (College of Electrical, Energy and Power Engineering, Yangzhou University, Huayang West Road 196, Yangzhou 225127, China)

  • Jiaquan Sun

    (College of Electrical, Energy and Power Engineering, Yangzhou University, Huayang West Road 196, Yangzhou 225127, China)

  • Jiangkuan Li

    (School of Information Engineering (School of Artificial Intelligence), Yangzhou University, Huayang West Road 196, Yangzhou 225127, China)

  • Jing Xu

    (School of Mechanical Engineering, Yangzhou University, Huayang West Road 196, Yangzhou 225127, China)

Abstract

The problem of insufficient signal coverage of 5G base stations can be solved by building new base stations in areas with weak signal coverage. However, due to construction costs and other factors, it is not possible to cover all areas. In general, areas with high traffic and weak coverage should be given priority. Although many scientists have carried out research, it is not possible to make the large-scale calculation accurately due to the lack of data support. It is necessary to search for the central point through continuous hypothesis testing, so there is a large systematic error. In addition, it is difficult to give a unique solution. In this paper, the weak signal coverage points were divided into three categories according to the number of users and traffic demand. With the lowest cost as the target, and constraints such as the distance requirement of base station construction, the proportion of the total signal coverage business, and so on, a single objective nonlinear programming model was established to solve the base station layout problem. Through traversal search, the optimal threshold of the traffic and the number of base stations was obtained, and then, a kernel function was added to the mean shift clustering algorithm. The center point of the new macro station was determined in the dense area, the location of the micro base station was determined from the scattered and abnormal areas, and finally the unique optimal planning scheme was obtained. Based on the assumptions made in this paper, the minimum total cost is 3752 when the number of macro and micro base stations were determined to be 31 and 3442 respectively, and the signal coverage rate can reach 91.43%. Compared with the existing methods, such as K-means clustering, K-medoids clustering, and simulated annealing algorithms, etc., the method proposed in this paper can achieve good economic benefits; when the traffic threshold and the number of base stations threshold are determined, the unique solution can be obtained.

Suggested Citation

  • Jian Chen & Yongkun Shi & Jiaquan Sun & Jiangkuan Li & Jing Xu, 2023. "Base Station Planning Based on Region Division and Mean Shift Clustering," Mathematics, MDPI, vol. 11(8), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1971-:d:1129728
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    References listed on IDEAS

    as
    1. Qiangqiang Chen & Linjie He & Yanan Diao & Kunbin Zhang & Guoru Zhao & Yumin Chen, 2022. "A Novel Neighborhood Granular Meanshift Clustering Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    2. Jian Chen & Jiajun Tian & Shuheng Jiang & Yunsheng Zhou & Hai Li & Jing Xu, 2022. "The Allocation of Base Stations with Region Clustering and Single-Objective Nonlinear Optimization," Mathematics, MDPI, vol. 10(13), pages 1-19, June.
    3. Min Wook Kang & Yun Won Chung, 2017. "An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization," Energies, MDPI, vol. 10(9), pages 1-28, September.
    4. Ting-Li Chen, 2015. "On the convergence and consistency of the blurring mean-shift process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 157-176, February.
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