Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 2 Oct 2025.
Builds on
7 of 19 references.
References · 7
- 2017Detecting causal associations in large nonlinear time series datasetscited by 244
- 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
- 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
- 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
- 2020Reconstructing regime-dependent causal relationships from observational time seriescited by 28
- 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacencycited by 6
- 2024Root Cause Analysis of Anomalies in 5G RAN Using Graph Neural Network and Transformercited by 4
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshootingcited by 2
- 2026A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracycited by 0
- 2026Inferring Causal Relations between Two Sequences of Events with Language Modelscited by 0
- 2026Inferring Causal Relations between Two Sequences of Events with Language Models30 Sep 2026
- 2026A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy7 Aug 2026
- 2025Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting1 Nov 2025