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.

翻译：无线接入网（RAN）配置传统上需要大量人工干预，原因在于可观测关键性能指标（KPI）之间存在间接因果依赖关系，且最优配置随网络工况变化而呈现情境相关特性。尽管近期数据驱动方法实现了参数调优改进，但在区分因果方向与统计相关性、跨多样化运行情境泛化方面仍存在局限。为应对这些挑战，我们提出BLINC（贝叶斯大语言模型驱动的智能网络配置）——一种将电信领域知识融入因果结构学习的大语言模型辅助贝叶斯网络框架。基于私有5G部署的训验结果表明，通过功率控制与链路自适应参数的联合优化，本方法在相较纯数据基线实现19.7%误块率降低的同时，吞吐量提升达63.5%。该框架不仅提供可解释的因果结构，还能量化预测不确定性。我们还展示了贝叶斯网络框架适应不同部署场景的能力，并提出一种含学习率的增量式条件概率分布（CPD）更新机制，支持模型随网络工况演变持续自适应。