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

• URL: http://arxiv.org/abs/2209.10428v1
• Date: Wed, 21 Sep 2022 15:21:59 GMT
• Title: An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
• Authors: Dimitrios Michael Manias, Ali Chouman, Abdallah Shami
• Abstract summary: 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.
• Score: 3.5573601621032935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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