Large Language Models for Automated Network Configuration and Troubleshooting
Dr. Peng Wang, Dr. Sarah Chen, Dr. Tom Miller
Bell Labs / Nokia
Abstract
This paper investigates the application of large language models (LLMs) to automated network configuration and troubleshooting in modern telecom networks. We fine-tune a 7B parameter LLM on a corpus of network configuration files, troubleshooting logs, and operator manuals. The fine-tuned model correctly diagnoses 82% of common network faults and generates valid configuration patches with 91% accuracy, significantly outperforming rule-based expert systems.
AI Summary
- Fine-tunes a 7B LLM on telecom-specific data for network operations automation.
- Achieves 82% fault diagnosis accuracy and 91% configuration patch accuracy.
- Outperforms traditional rule-based expert systems by a significant margin.
- Deployed in a pilot with three European operators.
Key Findings
- 1LLMs can understand complex network configurations across multiple vendor equipment.
- 2Chain-of-thought prompting improves diagnosis accuracy by 15% over direct prompting.
- 3The model learns implicit dependencies between configuration parameters.
Industry Implications
Could dramatically reduce mean time to repair (MTTR) in production networks.
Enables intent-based networking where operators describe desired outcomes in natural language.
Foundation for autonomous network operations envisioned in 6G architectures.
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