Exploration of NWDAF Development Architecture for 6G AI-Native Networks

doi:10.12142/ZTECOM.202501006

ZTE Communications ›› 2025, Vol. 23 ›› Issue (1): 45-52.DOI: 10.12142/ZTECOM.202501006

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Exploration of NWDAF Development Architecture for 6G AI-Native Networks

HE Shiwen¹^,²(), PENG Shilin¹, DONG Haolei¹, WANG Liangpeng², AN Zhenyu²

^1.School of Computer Science and Engineering, Central South University, Changsha 410083, China
^2.Purple Mountain Laboratories, Nanjing 210096, China

Received:2024-12-15 Online:2025-03-25 Published:2025-03-25
About author:HE Shiwen ( shiwen.he.hn@csu.edu.cn) is a professor at the School of Computer Science and Engineering, Central South University, China. His research interests include basic theoretical research and standard protocol development in wireless cellular/satellite/WLAN communication and networking, distributed learning and optimization computing, data mining and intelligent analysis, as well as research and development of low-level implementation theory and application technology for open programmable AI-native communication prototype systems.
PENG Shilin received his BS degree in IoT engineering from the School of Internet of Things Engineering, Hohai University, China in 2023. He is currently pursuing his MS degree in computer technology at Central South University, China. His research interest is AI-Native wireless communication.
DONG Haolei received his MS degree in computer science from the School of Computer Science, Wuhan University, China in 2019. He is currently pursuing his PhD degree in computer science at Central South University, China. His research interests include AI-Native wireless communication, 6G core networks, and knowledge graphs.
WANG Liangpeng is a senior engineer at Purple Mountain Laboratories (PML), China, specializing in wireless communication and network technologies. His research focuses on big data analytics and AI algorithms for networks, as well as knowledge graph-driven algorithms for autonomous network operations and intelligence.
AN Zhenyu is currently a senior engineer at Purple Mountain Laboratories (PML), China. His research interests include optimization theory and ultra-reliable and low latency communications.
Supported by:
the National Key Research and Development Program of China(2023YFE0200700);National Natural Science Foundation of China(62171474);ZTE Industry-University-Institute Cooperation Funds(IA20241014013)

Abstract

Abstract:

Artificial intelligence (AI)-native communication is considered one of the key technologies for the development of 6G mobile communication networks. This paper investigates the architecture for developing the network data analytics function (NWDAF) in 6G AI-native networks. The architecture integrates two key components: data collection and management, and model training and management. It achieves real-time data collection and management, establishing a complete workflow encompassing AI model training, deployment, and intelligent decision-making. The architecture workflow is evaluated through a vertical scaling use case by constructing an AI-native network testbed on Kubernetes. Within this proposed NWDAF, several machine learning (ML) models are trained to make vertical scaling decisions for user plane function (UPF) instances based on data collected from various network functions (NFs). These decisions are executed through the Kubernetes API, which dynamically allocates appropriate resources to UPF instances. The experimental results show that all implemented models demonstrate satisfactory predictive capabilities. Moreover, compared with the threshold-based method in Kubernetes, all models show a significant advantage in response time. This study not only introduces a novel AI-native NWDAF architecture but also demonstrates the potential of AI models to significantly improve network management and resource scaling in 6G networks.

Key words: 6G, AI-native, NWDAF, UPF scaling

HE Shiwen, PENG Shilin, DONG Haolei, WANG Liangpeng, AN Zhenyu. Exploration of NWDAF Development Architecture for 6G AI-Native Networks[J]. ZTE Communications, 2025, 23(1): 45-52.

Figures/Tables 7

Figure 1 System functional architecture

Figure 2 Data collection and management

Figure 3 Model training and management

Figure 4 System deployment structure

Figure 5 Vertical scaling of NF container

Figure 6 Data collection and algorithm comparison during the scaling process

Table 1 Prediction accuracy of the three models

Model	Accuracy/%
RNN	94.74
LSTM	94.87
XGBoost	86.84

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Exploration of NWDAF Development Architecture for 6G AI-Native Networks

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