基于LLM的NWDAF：迈向AI原生型6G网络智能化的关键一步
LLM-Enabled NWDAF: A Step Toward AI-Native 6G Network Intelligence
摘要
网络数据分析功能（NWDAF）对于实现第五代（5G）网络的零接触式网络管理至关重要，它支持实时分析与闭环自动化操作。尽管具有如此重要的作用，但开源版本的NWDAF在功能和可访问性方面仍然有限。本文中，我们开发了一种与开源核心网络Free5GC兼容的开源NWDAF，该工具通过订阅网络功能来收集网络数据，同时还包含集成的大型语言模型接口，使得人类操作员能够以自然语言进行交互。该接口能够处理用户的意图，使用语义嵌入模型对这些意图进行编码，并将其归类为七种预定义的意图类型之一，从而触发分析查询或订阅命令。这种架构能够简化传统接口的复杂性，使非专业用户也能轻松管理网络分析与订阅工作。该系统支持访问和管理功能以及会话管理功能的事件订阅、实时监控，还可以通过Prometheus进行数据分析的获取，所有功能都可以通过对话式界面实现。通过将人工智能驱动的意图识别与标准化的网络分析相结合，我们的实现方式提升了操作员的使用便利性，并为基于人工智能的6G网络提供了基础。当前研究过程中生成的源代码和数据集可以在github仓库中找到：https://github.com/HenokDanielbfg/testbed。
English Abstract
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.