Location Planning of 5G Base Station Based on Immune Algorithm and Clustering Algorithm
Yihang Wu, Yi Shen, Haoyi Fan · 2023
The problem of communication coverage is increasingly critical with the advancement of 5G communication technology. The reasonable establishment of new 5G base stations can effectively improve communication coverage. Consequently, the exploration and analysis of new 5G base stations have garnered significant attention and research interest. In this regard, location planning based on machine learning has been widely deployed and applied in a large number of practical large-scale construction schemes. However, its research and application in electronic communication and other emerging fields are few, and no specific reasonable explanation scheme is given. In this paper, we propose a DBSCAN clustering algorithm based on an immune algorithm and KD-Tree for location planning of 5G base stations. We transform the location problem of the 5G base station into a multi-objective problem with the largest coverage and the smallest total construction cost, set the corresponding constraint conditions, formulate the corresponding small base station replacement strategy according to the relationship between large base station and small base station, and finally apply the immune algorithm to solve the problem. In order to further solve the communication problem of weak coverage area, we employ the K-Means and DBSCAN clustering methods based on KD-Tree adjacency point queries to cluster these areas. The experimental results demonstrate that our model can offer an optimal location planning scheme for 5G base stations, achieving a high coverage rate of 93.73 %.