Intelligent Recommendation of Mobile Network Planning Areas Based on Multi-Dimensional Heterogeneous Data and AI Algorithm

In current mobile network planning and optimization works, especially regarding construction areas in network planning, it is difficult to conduct a checklist-style assessment and ranking of the value of selected scenarios due to the lack of quantifiable and unified evaluation standards. In order to identify the high-density distribution points of comprehensive network issues, a density-based clustering algorithm is introduced to cluster coverage problems, complaint problems, competitor problems, and valuable data areas in this paper. It aggregates and calculates the overall indicator characteristics for each clustered problem area. By computing the information entropy of different indicators for each area, the value weights of indicators in various dimensions are determined. Finally, a comprehensive score for the problem areas is calculated, thereby a comprehensive priority for these areas can be recommended to conduct network planning and optimization.