Locating the Root Cause of Poor Coverage in Mobile Communication Networks Based on Spatio-temporal Graph Message Propagation

Poor coverage quality is a common cause of poor wireless communication network quality, which seriously affects the user experience in mobile communication. Currently, the front line mainly adopts a manual trial-and-error method, which has problems such as low efficiency and high human cost. How to use artificial intelligence algorithms to quickly and accurately identify and solve the problem of poor coverage quality based on existing data is one of the important research directions in the field of wireless networks. The data of wireless networks is essentially spatio-temporal data, but most of the existing methods are based on time-domain and space-domain data for analysis and modeling, and the information mining in the spatio domain is not sufficient. In the spatio domain, the distribution of base stations is not uniform in Euclidean space, which increases the difficulty of spatio-temporal modeling. In view of the natural advantages of graph mining technology for modeling and processing unstructured data, this paper proposes a model named Spatio-Temporal Graph Message Propagation (STGMP) based on graph technology. This method uses spatio-temporal graphs to represent the historical states of related service cells, proposes a processing layer that combines the time and spatio domains, and maps the actual problem to a multi-classification task, thereby achieving the identification of the causes of poor coverage quality. This paper also conducts experiments on real data sets, and the results show that the proposed method STGMP is very effective.