Base Station Location Modeling and Signal Coverage Optimal Design Based on Genetic Algorithm

Abstract

With the development of mobile communication technology, the rapid information transmission of 5G communication technology at this stage is full of It meets people's increasing communication needs, but its large bandwidth and small base station coverage make the planning problem of communication network more and more complicated. Therefore, it is particularly important to optimize the deployment of base station location selection. The problem is a regional clustering problem based on distance and density, and the object of clustering is the given weak coverage points that exist in the area. The severity of the signal loss of each weak coverage point is visualized through the heat map, and each weak coverage point is defined as a label based on two levels of "signal loss degree" and "distance between sites", and then aggregated by DBSCAN. The class model performs hierarchical label search, and groups the points with the same two-layer label into one class, so as to realize the density-based regional clustering of weak coverage points. The model combines hierarchical clustering and density clustering to achieve the effect of multi-layer synchronous density clustering, making it faster in processing, and in terms of clustering results, the degree of signal loss and point distance Carry out more comprehensive clustering, so that the finally obtained clustering area points have stronger interrelationships and achieve better clustering effect.