An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | |
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| _version_ | 1866913252374478848 |
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| author | Manias, Dimitrios Michael Chouman, Ali Shami, Abdallah |
| author_facet | Manias, Dimitrios Michael Chouman, Ali Shami, Abdallah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_10428 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization Manias, Dimitrios Michael Chouman, Ali Shami, Abdallah Networking and Internet Architecture Machine Learning 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. |
| title | An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2209.10428 |