ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911073960984576 |
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| author | Oliveira, Daniel Ricardo Cunha Moreira, Rodrigo Silva, Flávio de Oliveira |
| author_facet | Oliveira, Daniel Ricardo Cunha Moreira, Rodrigo Silva, Flávio de Oliveira |
| contents | Artificial Intelligence (AI) plays a key role in developing 6G networks. While current specifications already include Network Data Analytics Function (NWDAF) as a network element responsible for providing information about the core, a more comprehensive approach will be needed to enable automation of network segments that are not yet fully explored in the context of 5G. In this paper, we present Automated Radio Coverage Anomalies Detection and Evaluation (ARCADE), a methodology for identifying and diagnosing anomalies in the cellular access network. Furthermore, we demonstrate how a hybrid architecture of network analytics functions in the evolution toward 6G can enhance the application of AI in a broader network context, using ARCADE as a practical example of this approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks Oliveira, Daniel Ricardo Cunha Moreira, Rodrigo Silva, Flávio de Oliveira Networking and Internet Architecture Emerging Technologies Artificial Intelligence (AI) plays a key role in developing 6G networks. While current specifications already include Network Data Analytics Function (NWDAF) as a network element responsible for providing information about the core, a more comprehensive approach will be needed to enable automation of network segments that are not yet fully explored in the context of 5G. In this paper, we present Automated Radio Coverage Anomalies Detection and Evaluation (ARCADE), a methodology for identifying and diagnosing anomalies in the cellular access network. Furthermore, we demonstrate how a hybrid architecture of network analytics functions in the evolution toward 6G can enhance the application of AI in a broader network context, using ARCADE as a practical example of this approach. |
| title | ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks |
| topic | Networking and Internet Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2507.17861 |