Anomaly Detection and Root Cause Analysis Enabled by Artificial Intelligence

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Traditional anomaly detection and root cause analysis in radio access network are not accurate and efficient enough to enable automatic network operation and maintenance in large-scale 4G/5G heterogeneous network. In this paper, we designed a framework of anomaly detection and root cause analysis, which was used in the field trial. In the framework, we proposed an algorithm of anomaly detection together with a labeled dataset of radio access network. The algorithm is proved to be more efficient with slightly performance loss compared with the existing State-of-the-Art algorithm. In addition, the automatic network closed-loop method for capacity problem, including the proposed anomaly detection and root cause analysis, has been verified in several base station sites based on O-RAN architecture.