Research on indoor deep coverage of communication networks: challenges and solutions

More than 75% of communication services occur indoors. Therefore, indoor deep coverage is one of the most important tasks in network operation and maintenance. Indoor deep coverage is confronted with challenges such as the inability to traverse tests, the lack of a unified analysis system, and the absence of effective production tools. This paper proposes a technical architecture based on digitalization and intelligence. Based on the multi-dimensional data of the current network, an analysis technology system is formed through association rules and deep learning. The deterministic prediction method is utilized to construct the hierarchical quantitative evaluation capability of buildings. Establish the joint optimization objective function and adopt the particle swarm intelligent optimization algorithm to implement the deep coverage scheme. Through verification in the pilot area, this technical solution can increase the indoor depth coverage by 32.99% without adding additional base station resources.