Reasoning Language Models for Root Cause Analysis in 5G Wireless
  Networks
Authors: name: Mohamed Sana, name: Nicola Piovesan, name: Antonio De Domenico, name: Yibin Kang, name: Haozhe Zhang, name: Merouane Debbah, name: Fadhel Ayed

Root Cause Analysis (RCA) in mobile networks remains a challenging task due
to the need for interpretability, domain expertise, and causal reasoning. In
this work, we propose a lightweight framework that leverages Large Language
Models (LLMs) for RCA. To do so, we introduce TeleLogs, a curated dataset of
annotated troubleshooting problems designed to benchmark RCA capabilities. Our
evaluation reveals that existing open-source reasoning LLMs struggle with these
problems, underscoring the need for domain-specific adaptation. To address this
issue, we propose a two-stage training methodology that combines supervised
fine-tuning with reinforcement learning to improve the accuracy and reasoning
quality of LLMs. The proposed approach fine-tunes a series of RCA models to
integrate domain knowledge and generate structured, multi-step diagnostic
explanations, improving both interpretability and effectiveness. Extensive
experiments across multiple LLM sizes show significant performance gains over
state-of-the-art reasoning and non-reasoning models, including strong
generalization to randomized test variants. These results demonstrate the
promise of domain-adapted, reasoning-enhanced LLMs for practical and
explainable RCA in network operation and management.