Modern 5G/6G radio access networks are increasingly programmable through O-RAN, yet their operational complexity has grown with disaggregation, open interfaces, and fine-grained control parameters. While RAN-side analytics and telemetry mechanisms, such as KPI-based monitoring and mobility event reporting, provide visibility into network behavior, operators still face challenges in correlating heterogeneous events and safely translating observations into actionable configuration changes. This paper presents an LLM-based Net Analyzer rApp for the O-RAN Non-RT RIC that enables explainable and safe, human-in-the-loop automation for RAN operations. The proposed rApp adopts an event-informed, batch-triggered reasoning framework in which mobility events are first interpreted, anomalies are confirmed through targeted log inspection, configurations are inspected via tool-gated access, and minimal configuration changes are proposed only after explicit operator approval. The architecture enforces a strict separation between reasoning and actuation, ensuring auditability and operational safety. The system is implemented and demonstrated on a real O-RAN testbed using a reproducible ping-pong handover scenario, illustrating how large language models can function as reasoning co-pilots that transform raw RAN telemetry into structured explanations and controlled remediation workflows, complementing existing analytics-only approaches in the NonRT RIC.

翻译：现代5G/6G无线接入网络通过O-RAN日益可编程化，但随着网络解耦、开放接口和细粒度控制参数的应用，其运营复杂性也相应增加。尽管基于KPI的监控和移动性事件上报等无线接入网侧分析与遥测机制为网络行为提供了可见性，但运营商在关联异构事件以及安全地将观测结果转化为可执行的配置变更方面仍面临挑战。本文提出一种用于O-RAN非实时RIC的基于大语言模型的网络分析器rApp，能够为无线接入网运营提供可解释且安全的人机协同自动化方案。该rApp采用事件驱动、批量触发的推理框架：首先解析移动性事件，通过定向日志检查确认异常，借助工具门控访问机制审查配置，仅在获得操作员明确批准后才提出最小化的配置变更建议。该架构严格执行推理与执行分离，确保可审计性与运营安全性。我们在真实O-RAN测试平台上通过可复现的乒乓切换场景实现并演示了该系统，展示了大语言模型如何作为推理协作者，将原始无线接入网遥测数据转化为结构化解释与受控的修复工作流，从而对非实时RIC中现有纯分析方案形成有效补充。