LLM-Enabled NWDAF: A Step Toward AI-Native 6G Network Intelligence

2026-06-10

AI Summary

This work addresses the limitations of existing open-source NWDAF systems, which offer constrained functionality and complex interfaces that hinder accessibility for non-expert users. To bridge this gap, we propose the first open-source NWDAF architecture integrating a large language model (LLM), fully compatible with Free5GC. Our approach leverages semantic embedding and intent classification to map natural language commands to predefined operations, enabling real-time subscription to and querying of AMF/SMF events. The system incorporates Prometheus-based monitoring and adheres to 3GPP-standard NWDAF interfaces, supporting conversational interaction and substantially lowering the barrier to entry. Both the implementation code and associated dataset are publicly released to foster the evolution of AI-native network management for 6G.

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.

Problem

• NWDAF
• AI-native
• 6G
• natural language interaction
• zero-touch network management

Innovation

• LLM-integrated NWDAF
• AI-native 6G
• natural language interface
• intent recognition
• open-source 5G core