Generative AI for Anomaly Diagnosis in 5G NR Scheduling Logs

Sammanfattning

Fifth-Generation New Radio (5G NR) base stations produce detailed scheduling logs that record per-slot decisions, channel measurements, and Hybrid Automatic Repeat Request (HARQ) feedback at sub-millisecond granularity. Diagnosing anomalies in these logs currently requires domain experts to spend approximately one hour of manual inspection per file, and no standardised tooling exists for automated Root Cause Analysis (RCA). This thesis presents an end-to-end automated diagnostic system whose main contribution is the integration of statistical anomaly detection, correlation-based root-cause differentiation, and Generative Artificial Intelligence (GenAI) explanation into a single pipeline for 5G NR scheduling logs. Raw binary traces are parsed into a relational database, where statistical and Machine Learning (ML) methods detect anomalies at both the individual-record and temporal levels. A correlation-based module differentiates root causes that exhibit similar signatures, a rule engine maps findings to verified diagnostic patterns, and a GenAI agent powered by a Large Language Model (LLM) synthesises the collected evidence into readable diagnostic reports. A feedback mechanism allows engineers to confirm or correct diagnoses, accumulating a case store for future reference. The system was evaluated as a proof-of-concept on 11 scheduling logs covering multiple distinct anomaly scenarios in a controlled laboratory environment. Compared with unassisted manual diagnosis, the system correctly classified all observed anomaly patterns, identified two engineer-confirmed failures, and uncovered subtle events that conventional threshold-based alarms missed. Diagnosis time dropped from approximately one hour to a few minutes per log. Although the evaluation is confined to a laboratory setting with a small sample size, the results confirm that combining ML-based detection with LLM-driven explanation is viable for operational 5G log analysis.