# Towards NWDAF-enabled Analytics and Closed-Loop Automation in 5G Networks

arXiv: https://arxiv.org/abs/2505.06789 · HTML: https://mlsanity.com/paper/2505.06789/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2025.

## Built on by (most cited first, 4 of 4)

- [MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking](https://mlsanity.com/paper/2602.14283/) (2026)
- [You Need Reasoning to Learn Reasoning: The Limitations of Label-Free RL in Weak Base Models](https://mlsanity.com/paper/2511.04902/) (2025)
- [IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks](https://mlsanity.com/paper/2601.13114/) (2026)
- [Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks](https://mlsanity.com/paper/2607.25989/) (2026)

## Abstract (arXiv)

The fifth generation of cellular technology (5G) delivers faster speeds, lower latency, and improved network service alongside support for a large number of users and a diverse range of verticals. This brings increased complexity to network control and management, making closed-loop automation essential. In response, the 3rd Generation Partnership Project (3GPP) introduced the Network Data Analytics Function (NWDAF) to streamline network monitoring by collecting, analyzing, and providing insights from network data. While prior research has focused mainly on isolated applications of machine learning within NWDAF, critical aspects such as standardized data collection, analytics integration in closed-loop automation, and end-to-end system evaluation have received limited attention. This work addresses existing gaps by presenting a practical implementation of NWDAF and its integration with leading open-source 5G core network solutions. We develop a 3GPP-compliant User Plane Function (UPF) event exposure service for real-time data collection and an ML model provisioning service integrated with MLflow to support end-to-end machine learning lifecycle management. Additionally, we enhance the Session Management Function (SMF) to consume NWDAF analytics and respond accordingly. Our evaluation demonstrates the solution's scalability, resource efficiency, and effectiveness in enabling closed-loop security management in 5G networks.