LLMs Enhance Telecom Root Cause Analysis with Structured Reasoning

Diagnosing performance degradations in complex telecom networks, especially with 5G and emerging 6G technologies, is a critical and challenging task due to intricate cross-layer dependencies. While Large Language Models (LLMs) offer promising capabilities for reasoning and integrating knowledge, their direct application to telecom Root Cause Analysis (RCA) often leads to issues such as hallucinations, unstable reasoning, and a lack of alignment with structured network evidence. This research reviews the evolution of telecom RCA, from traditional rule-based and machine learning approaches to modern LLM-enabled techniques, highlighting recent paradigms like structured reasoning, retrieval-augmented knowledge grounding, agentic orchestration, and verifiable reasoning. Building on these insights, the paper introduces a novel structured reasoning framework specifically designed for LLM-enabled telecom RCA. The proposed framework systematically organizes heterogeneous network telemetry into canonical contexts. It then enforces a decision-path reasoning process during diagnosis, ensuring that the LLM's conclusions are logically derived. Finally, it generates explanations that are explicitly grounded in the available evidence, enhancing reliability and trust. Experimental evaluations on two 5G RCA datasets, TeleLogs and TelecomTS, demonstrate that this structured reasoning framework consistently improves both diagnostic accuracy and decision consistency compared to baseline techniques. These cross-dataset results underscore the vital role of structured reasoning in developing practical and dependable LLM-based RCA systems for next-generation telecom networks.