{"ID":2861513,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17505","arxiv_id":"2511.17505","title":"Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks","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.","short_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 e...","url_abs":"https://arxiv.org/abs/2511.17505","url_pdf":"https://arxiv.org/pdf/2511.17505v1","authors":"[\"Chenhua Shi\",\"Joji Philip\",\"Subhadip Bandyopadhyay\",\"Jayanta Choudhury\"]","published":"2025-10-02T14:34:59Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.LG\"]","methods":"[]","has_code":false}