Fast and Accurate Root Cause Analysis Based on Signalling Messages for 5G Networks

Zhaorui Guo, Jiyan Sun, Jiadong Fu, Lu Yuan, Shangyuan Zhuang, Liru Geng, Yinlong Liu, Wei Ma

Abstract: The ever-increasing complexity and scale of 5G communication networks pose huge challenges to network operations. Root cause analysis is considered as a promising method for fault detection. However, it still suffers challenges of severely uneven distribution of fault data, low accuracy in root cause detection, and long time consumption due to a large search space in 5G cellular networks. To address the above challenges, we introduce SimRCA to effectively analyze the faults’ root causes in 5G networks using signalling messages. By designing a novel confidence threshold value and pruning technique, SimRCA can significantly reduce the search space of signalling messages while maintaining the accuracy of root cause analysis. Moreover, SimRCA is proven to be able to handle unbalanced data distribution in 5G networks. We collected over 10GB of signalling data from Huawei 5G commercial network and conducted extensive experiments on this dataset. Experimental results demonstrate that SimRCA can complete root cause localization and fault classification within 11 seconds with an average F1-score over 0.93 which outperforms the current state-of-the-art solutions.