5G Base Station Deployment Based on Improved Hierarchical Co-Evolutionary Immunization Algorithm

With the development of mobile communication technology and the complexity of communication networks, the coverage area of base stations decreases and the demand becomes larger. In this paper, a corresponding planning model is established and an intelligent optimization algorithm is used to solve the problem. Firstly, the data are cleaned and denoised, and the data that do not meet the requirements are eliminated by calculating the Euclidean distance between the existing network base stations and the weak coverage points. Then the K-means algorithm is used to cluster the samples according to the coverage of the base stations. Then a planning model is established and solved using a hierarchical coevolutionary immune algorithm to finally calculate the number of base stations that need to be built. When the coverage of the base station changes from the center of a circle to an angle-limited sector. A functional analysis is performed for the coverage of the base stations. Using linear interpolation, the relationship between the coverage radius and the angle of the base station direction is obtained. The planning model is established by taking the sector coverage angle in the main direction as a constraint. The maximum coverage of a single base station that can be obtained at an angle of 120° is obtained, and it is found that the coverage decreases under the actual condition compared with the previous one.