# An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization

arXiv: https://arxiv.org/abs/2209.10428 · HTML: https://mlsanity.com/paper/2209.10428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Concept Drift Detection in Federated Networked Systems](https://mlsanity.com/paper/2109.06088/) (2021)
- [Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis](https://mlsanity.com/paper/2205.15121/) (2022)

## Built on by (most cited first, 2 of 2)

- [Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks](https://mlsanity.com/paper/2403.02238/) (2024)
- [Towards Network Data Analytics in 5G Systems and Beyond](https://mlsanity.com/paper/2506.04860/) (2025)

## Abstract (arXiv)

Data-driven approaches and paradigms have become promising solutions to efficient network performances through optimization. These approaches focus on state-of-the-art machine learning techniques that can address the needs of 5G networks and the networks of tomorrow, such as proactive load balancing. In contrast to model-based approaches, data-driven approaches do not need accurate models to tackle the target problem, and their associated architectures provide a flexibility of available system parameters that improve the feasibility of learning-based algorithms in mobile wireless networks. The work presented in this paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the Network Data Analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition. Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.