Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis
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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.
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
5 of 18 references.
References · 5
- 2019Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6Gcited by 25
- 2019Towards 6G Networks: Use Cases and Technologiescited by 14
- 2014NFV: State of the Art, Challenges and Implementation in Next Generation Mobile Networks (vEPC)cited by 3
- 2020Machine Learning for Performance-Aware Virtual Network Function Placementcited by 3
- 2020The Need for Advanced Intelligence in NFV Management and Orchestrationcited by 3
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Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 1; 2026: 2.
7 papers.
- 2024Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networkscited by 17
- 2026MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networkingcited by 3
- 2022Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networkscited by 2
- 2022An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterizationcited by 2
- 2026AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networkscited by 1
- 2024Attacks Against Mobility Prediction in 5G Networkscited by 0
- 2025Towards Network Data Analytics in 5G Systems and Beyondcited by 0
- 2026AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks24 Mar 2026
- 2026MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking15 Feb 2026
- 2025Towards Network Data Analytics in 5G Systems and Beyond5 Jun 2025
- 2024Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks4 Mar 2024
- 2024Attacks Against Mobility Prediction in 5G Networks29 Feb 2024
- 2022An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization21 Sep 2022
- 2022Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks22 Jul 2022