大型語言模型賦能的NWDAF：邁向原生AI的第六代網路智慧

摘要

網路資料分析功能（NWDAF）是實現第五代（5G）網路零接觸網路管理的核心，透過支援即時分析與閉環自動化達成此目標。儘管其角色關鍵，但開源NWDAF實作在範圍與可及性上仍有限。在本論文中，我們開發了一套與開源核心網路Free5GC相容的開源NWDAF，該功能透過訂閱網路功能（NFs）收集網路資料，並整合大語言模型（LLM）介面，使人類操作員得以透過自然語言進行互動。該介面處理使用者意圖，利用語義嵌入模型進行編碼，並將其映射至七個預定義意圖類別之一，以觸發分析查詢或事件訂閱指令。此架構抽象化傳統介面的複雜性，使非專家使用者能輕鬆管理網路分析與訂閱。系統支援存取與移動性管理功能（AMF）及會話管理功能（SMF）的事件訂閱、即時監控，並可透過Prometheus擷取分析數據，所有功能皆可透過對話式介面存取。透過將AI驅動的意圖識別與標準化網路分析結合，我們的實作提升了操作員的可用性，並為邁向AI原生6G網路奠定基礎。本研究所產生的原始碼與資料集已存放於GitHub儲存庫：https://github.com/HenokDanielbfg/testbed。

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.