大语言模型赋能的NWDAF：迈向AI原生6G网络智能的一步

摘要

网络数据分析功能（NWDAF）是实现第五代（5G）网络零接触网络管理的核心，它支持实时分析和闭环自动化。尽管其关键作用至关重要，但开源NWDAF实现的范围和可访问性仍然有限。本文开发了一种与开源核心网络Free5GC兼容的开源NWDAF，通过订阅网络功能（NF）收集网络数据，并集成了大型语言模型（LLM）接口，支持人类操作员进行自然语言交互。该接口处理用户意图，使用语义嵌入模型进行编码，并将其映射到七个预定义的意图类别之一，以触发分析查询或事件订阅命令。该架构抽象了传统接口的复杂性，使非专业用户能够轻松管理网络分析和订阅。该系统支持访问与移动性管理功能（AMF）和会话管理功能（SMF）的事件订阅、实时监控以及通过Prometheus获取分析结果，所有功能均可通过对话式界面访问。通过将AI驱动的意图识别与标准化网络分析相结合，我们的实现增强了操作员的可用性，并为迈向AI原生的6G网络奠定了基础。本研究生成的源代码和数据集可在GitHub仓库（https://github.com/HenokDanielbfg/testbed）中获取。

English

The Network Data Analytics Function (NWDAF) is central to enabling zero-touch network management in fifth-generation (5G) networks by supporting real-time analytics and closed-loop automation. Despite its critical role, open-source NWDAF implementations remain limited in scope and accessibility. In this paper, we develop an open-source NWDAF, compatible with the open-source core network Free5GC, that collects network data via subscriptions to Network Functions (NFs), and also includes an integrated Large Language Model (LLM) interface that enables natural language interaction with human operators. The interface processes user intents, encodes them using a semantic embedding model, and maps them to one of seven predefined intent categories to trigger analytics queries or event subscription commands. This architecture abstracts the complexity of traditional interfaces, allowing non-expert users to manage network analytics and subscriptions with ease. The system supports Access and Management Function (AMF) and Session Management Function (SMF) event subscriptions, real-time monitoring, and analytics retrieval via Prometheus, all accessible through a conversational interface. By bridging AI-driven intent recognition with standardized network analytics, our implementation enhances operator usability and provides a foundation towards AI-native 6G networks. The source code and datasets generated during the current study are available in the github repository, https://github.com/HenokDanielbfg/testbed.