Root Cause Analysis of 5G Base Station Faults Based on Catboost Algorithm

Intelligent fault demarcation and locating technology for 5G base stations is a key technology for intelligent wireless networks. Currently, base station fault analysis relies on expert experience, board status, base station power environment data, and base station fault types, which is inefficient. The problem is not handled in a timely manner, which greatly affects customer experience. This paper uses the historical alarm data of 5G base stations and the marked data of historical processing alarm work orders, uses TF-IDF and Word2vec technology to create feature information data for alarm information, and uses Catboost algorithm to build AI model to automatically learn the hidden feature information in the data. Intelligent demarcation and locating of 5G base station fault alarms and prediction of handling solutions.