Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks

TL;DR

This paper presents an AI/ML pipeline that identifies root causes and the sequence of events leading to SLA breaches in Radio Access Networks, enabling proactive fault management.

Contribution

It introduces a novel causal intervention sequence analysis method that accurately uncovers trigger sequences in large-scale network data.

Findings

High precision in identifying correct trigger sequences

Scales efficiently to millions of data points

Enables proactive fault prevention

Abstract

To keep modern Radio Access Networks (RAN) running smoothly, operators need to spot the real-world triggers behind Service-Level Agreement (SLA) breaches well before customers feel them. We introduce an AI/ML pipeline that does two things most tools miss: (1) finds the likely root-cause indicators and (2) reveals the exact order in which those events unfold. We start by labeling network data: records linked to past SLA breaches are marked `abnormal', and everything else `normal'. Our model then learns the causal chain that turns normal behavior into a fault. In Monte Carlo tests the approach pinpoints the correct trigger sequence with high precision and scales to millions of data points without loss of speed. These results show that high-resolution, causally ordered insights can move fault management from reactive troubleshooting to proactive prevention.