BLINC: Context-Specific Causal Learning for Automated RAN Configuration

TL;DR

BLINC is a novel LLM-assisted Bayesian Network framework that enhances RAN configuration by learning causal structures, improving network throughput, and adapting to changing conditions with interpretability and uncertainty quantification.

Contribution

This work introduces BLINC, integrating domain knowledge into causal learning for RAN, achieving significant performance gains and enabling continuous adaptation.

Findings

Achieved 63.5% throughput improvement over baselines.

Reduced block error rate by 19.7%.

Demonstrated effective adaptation to different deployment scenarios.

Abstract

Radio Access Network (RAN) configuration has traditionally required significant manual effort due to indirect causal dependencies between observable Key Performance Indicators (KPIs), and context-dependent characteristics, where the optimal configurations vary with network conditions. Although recent data-driven approaches improve parameter tuning, they remain limited in distinguishing causal direction from statistical correlation and in generalizing across diverse operating contexts. To address these challenges, we propose BLINC (Bayesian Large Language Model (LLM)-Driven Intelligent Network Configuration), an LLM-assisted Bayesian Network framework that integrates telecommunications domain knowledge into causal structure learning. Trained and validated on a private 5G deployment, our method achieves throughput improvement of 63.5% with 19.7% reduction on block error rate over data-only baselines through joint optimization of power control and link adaptation parameters. The framework provides interpretable causal structure, while also quantifying prediction uncertainty. We also demonstrate the ability of the Bayesian Network framework to adapt to different deployment scenarios and propose an incremental Conditional Probability Distribution (CPD) update mechanism with learning rate for continuous model adaptation as network conditions evolve.