5G RAN Anomaly Prediction Using AI and ML

With advancement in the technology, from 2G to 5G [TS 38.413: 5G NG-RAN; NG Application Protocol (NGAP)], there is a dedicated effort by the operators and the vendors to reduce the outage caused due to various reasons in the field and ensure service availability. In this regard, many tools were developed to aid in analysis of the problems through logs and other OAM counters to prevent them in future. AI/ML (Linin arXiv:2305.05092, 2023) has been applied to solve many problems across different domains and in this whitepaper, we discuss on how this (AI/ML) could help us solve RAN outage related issues by predicting them based on pattern analysis in a cost optimal manner. The key aspect of the solution discussed in this whitepaper is about anomaly predictions based on different logging files (e.g., cell trace, subscriber trace, etc.,). The early prediction of traffic failure patterns would aid in traffic steering action from the operators thus avoiding outage and ensuring high QoE (quality of experience) to the end user of the service. We shall also analyze how the proposed solution could be cost effective, vendor agnostics framework to analyze the 3GPP standard defined Subscriber, Cell, and Equipment traces.