# Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis

arXiv: https://arxiv.org/abs/2205.15121 · HTML: https://mlsanity.com/paper/2205.15121/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 5 of 5)

- [Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G](https://mlsanity.com/paper/1907.07862/) (2019)
- [Towards 6G Networks: Use Cases and Technologies](https://mlsanity.com/paper/1903.12216/) (2019)
- [NFV: State of the Art, Challenges and Implementation in Next Generation Mobile Networks (vEPC)](https://mlsanity.com/paper/1409.4149/) (2014)
- [Machine Learning for Performance-Aware Virtual Network Function Placement](https://mlsanity.com/paper/2001.07787/) (2020)
- [The Need for Advanced Intelligence in NFV Management and Orchestration](https://mlsanity.com/paper/2008.00937/) (2020)

## Built on by (most cited first, 7 of 7)

- [Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks](https://mlsanity.com/paper/2403.02238/) (2024)
- [MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking](https://mlsanity.com/paper/2602.14283/) (2026)
- [Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks](https://mlsanity.com/paper/2207.11117/) (2022)
- [An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization](https://mlsanity.com/paper/2209.10428/) (2022)
- [AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks](https://mlsanity.com/paper/2603.23772/) (2026)
- [Attacks Against Mobility Prediction in 5G Networks](https://mlsanity.com/paper/2402.19319/) (2024)
- [Towards Network Data Analytics in 5G Systems and Beyond](https://mlsanity.com/paper/2506.04860/) (2025)

## Abstract (arXiv)

Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices. Requirements such as high data rates, low latencies, and reliability are crucial considerations and artificial intelligence is incorporated to achieve these requirements for a large number of connected devices. Specifically, intelligent methods and frameworks for advanced analysis are employed by the 5G Core Network Data Analytics Function (NWDAF) to detect patterns and ascribe detailed action information to accommodate end users and improve network performance. To this end, the work presented in this paper incorporates a functional NWDAF into a 5G network developed using open source software. Furthermore, an analysis of the network data collected by the NWDAF and the valuable insights which can be drawn from it have been presented with detailed Network Function interactions. An example application of such insights used for intelligent network management is outlined. Finally, the expected limitations of 5G networks are discussed as motivation for the development of 6G networks.