BLINC: Context-Specific Causal Learning for Automated RAN Configuration

2026-04-29

AI Summary

This work addresses the limitations of traditional radio access network (RAN) configuration, which relies heavily on manual expertise, struggles to uncover causal relationships among key performance indicators (KPIs), and exhibits poor generalization. To overcome these challenges, the authors propose BLINC, a novel framework that uniquely integrates large language models (LLMs) with Bayesian networks to inject domain-specific communication knowledge into causal structure learning, enabling context-aware, automated RAN optimization. BLINC generates interpretable causal graphs and introduces a learning-rate-based mechanism for incrementally updating conditional probability distributions, allowing adaptation to dynamic network conditions. Evaluated in a real-world 5G deployment, BLINC achieves a 63.5% throughput gain and a 19.7% reduction in block error rate, significantly outperforming purely data-driven baselines.

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.

Problem

• RAN configuration
• causal learning
• context-specific
• KPIs
• generalization

Innovation

• Causal Learning
• Bayesian Network
• LLM-assisted Optimization
• RAN Configuration
• Context Adaptation