LLM-assisted gNB Parameter Configuration for Radio Access Network

Researchers propose an LLM-assisted framework that automatically diagnoses and corrects gNB (base station) parameter misconfigurations in radio access networks by generating synthetic training data and fine-tuning language models. The approach achieves 92.7% accuracy in identifying corrective actions, potentially enabling autonomous RAN operation without manual intervention.

Radio access network (RAN) operations have historically relied on manual troubleshooting by skilled engineers who must parse complex logs to identify misconfigured gNB parameters. This labor-intensive process creates bottlenecks as networks scale and become more complex. The proposed LLM-assisted framework addresses this by automating the diagnosis-to-correction pipeline using synthetic data generation, where a commercial LLM creates realistic misconfiguration scenarios paired with corrective actions based on technical specifications.

The breakthrough lies in the synthetic data methodology. Rather than requiring expensive labeled datasets of real network failures, the framework generates training examples from working configurations and technical documentation, then uses retrieval-augmented generation (RAG) to ground model outputs in authoritative gNB specifications. Testing on 480 unseen misconfiguration scenarios demonstrates practical viability: zero-shot performance of 13.8% jumps to 85.4% after fine-tuning, with RAG pushing accuracy to 92.7%.

For telecom operators and RAN vendors, this represents significant operational value. Autonomous configuration correction reduces mean-time-to-recovery (MTTR), minimizes service disruptions, and decreases dependency on specialized technical staff. The framework's ability to generate deployable configurations suggests enterprise-ready potential. However, real-world deployment requires validation across diverse hardware vendors, geographic regions, and evolving 5G/6G standards.

The approach establishes a template for LLM application in infrastructure automation where synthetic training data overcomes scarcity of labeled real-world examples. Success here may encourage similar frameworks for network optimization, capacity planning, and security incident response, fundamentally reshaping how telecom networks are operated.

• →LLM fine-tuning achieves 92.7% accuracy in automated gNB parameter correction, eliminating manual network troubleshooting for common misconfiguration scenarios.
• →Synthetic data generation from technical specifications enables training without expensive labeled datasets of real network failures.
• →Retrieval-augmented generation grounds model outputs in authoritative documentation, ensuring generated configurations are valid and deployable.
• →Framework reduces operational burden on telecom operators by automating diagnosis and correction of base station misconfigurations at scale.
• →Success demonstrates practical viability of LLMs for infrastructure automation in telecommunications, with potential extension to broader RAN management tasks.