Root Cause Analysis of Faults in Power Grids 5G Network Based on RRC Signalling Messages

The growing flexibility of 5G network architectures increases the risk of network faults. Such network fault types are diverse and variability, as 5G networks have been integrated in various vertical industries, e.g., power grids, resulting in these faults being widespread and challenging to diagnose. To reduce the costs associated with fault identification and remediation, we introduce Rsm-RCA as a novel framework for automated root cause analysis (RCA) in 5G networks. By training on massive amounts of data, Rsm-RCA is capable of identifying fault types from the fault signalling messages collected. Specifically, this framework efficiently collects and processes radio resource control (RRC) fault signalling messages from commercial networks to extract multidimensional attributes and KPI parameters. The processed signalling messages are then fed into a decision tree model, which enables accurate fault classification with minimal time expenditure after training. For power utilities private 5G networks, fast and accurate RCA is highly beneficial. Experimental results demonstrate that Rsm-RCacan classify faults in an extremely short time, achieving an average accuracy of 99 %.