Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

2025-07-29

AI Summary

Existing root cause analysis (RCA) methods for 5G wireless networks suffer from poor interpretability, difficulty integrating domain knowledge, and insufficient causal reasoning. Method: We propose a lightweight multi-step diagnostic reasoning framework: (1) we construct TeleLogs—the first annotated log dataset specifically designed for RCA; (2) we introduce a two-stage training paradigm combining supervised fine-tuning and reinforcement learning; and (3) we explicitly embed structured reasoning generation and telecommunication domain knowledge into large language models (LLMs) to enhance transparency and logical rigor in diagnosis. Results: Our framework achieves state-of-the-art performance across multiple LLM scales, significantly outperforming both reasoning-based and non-reasoning baselines in accuracy, interpretability, and generalization to stochastic fault variants. Empirical evaluation confirms its practical utility and robustness in real-world 5G network operations.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Causality
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

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.

Problem

• Enhancing interpretability in 5G network root cause analysis
• Improving LLMs' causal reasoning for domain-specific RCA
• Generating structured diagnostic explanations for network troubleshooting

Innovation

• Lightweight LLM framework for RCA
• Two-stage training with fine-tuning and RL
• Domain-adapted models for structured diagnostics