用大模型自动修复5G基站配置错误，准确率超90%。
LLM-assisted gNB Parameter Configuration for Radio Access Network
- 用大模型生成虚假日志和配置，训练纠错模型。
- 在480种未见过的错误场景中，纠错准确率达92.7%。
- 适合网络运维人员和自动化系统研发者使用。
gNB参数配置错误是无线接入网（RAN）系统故障的常见原因，其诊断与修正依赖于复杂网络日志的手动分析，难以扩展。本文提出一种基于大语言模型（LLM）的自动gNB参数配置框架。该框架采用配置-日志-修正的工作流，结合可运行配置与gNB技术文档，利用商用大模型生成修改后的配置，并从错误日志中提取结构化推理轨迹。合成训练数据将网络状态映射至纠正动作，用于微调LLM实现配置修正。推理阶段，微调后的LLM可从错误日志生成有效且可部署的gNB参数配置。在基于OpenAirInterface（OAI）的gNB测试平台上的验证显示，微调使纠错准确率从零样本基线的13.8%提升至85.4%，引入检索增强生成（RAG）后进一步提升至92.7%。结果表明，该框架可实现无需人工干预的自动恢复，支持可扩展、自主的RAN运行。
原文摘要 · Abstract (English)
gNB parameter misconfigurations are a common cause of system failures in radio access networks (RANs), and their diagnosis and correction rely on manual analysis of complex network logs that does not scale well. This paper proposes a large language model (LLM)-assisted framework for automatic gNB parameter configuration. The framework adopts a synthetic data generation pipeline following a configuration, log, correction workflow. Starting from a workable configuration and the gNB technical references, the pipeline uses a commercial LLM to generate modified configurations and derive structured reasoning traces from gNB error logs. The synthetic training data maps network states to corrective actions and is used to fine-tune an LLM for configuration correction. During inference, the fine-tuned LLM generates valid and deployable gNB parameter configurations from gNB error logs. The framework is validated on an OpenAirInterface (OAI) gNB testbed with 480 unseen misconfiguration scenarios, where fine-tuning improves correction accuracy from 13.8% (zero-shot baseline) to 85.4%, and retrieval-augmented generation (RAG) further improves accuracy to 92.7%. The results demonstrate that the framework may enable automated recovery from misconfigurations without manual intervention and supports scalable and autonomous RAN operation.
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