ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks
Resumo
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.
Palavras-chave:
6G, Artificial Intelligence, AI, Mobile Networks, RAN, ARCADE, 5G
Referências
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Carmack, J., et al. (2021). Neural network generative models for radio frequency data. In 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0577–0582). IEEE.
Cheerla, S., Ratnam, D. V., & Borra, H. S. (2018). Neural network-based path loss model for cellular mobile networks at 800 and 1800 MHz bands. AEU - International Journal of Electronics and Communications, 94, 179–186.
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Gehlot, A., et al. (2022). Application of neural network in the prediction models of machine learning based design. In 2022 ICSES (pp. 1–6). IEEE.
Jeon, Y., Jeong, H., Seo, S., Kim, T., Ko, H., & Pack, S. (2022). A distributed NWDAF architecture for federated learning in 5G. In 2022 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1–2).
Kirkwood, C., Economou, T., Pugeault, N., & Odbert, H. (2022). Bayesian deep learning for spatial interpolation in the presence of auxiliary information. Mathematical Geosciences, 54(3), 507–531.
Neto, N. S., Gonçalves, M., Oliveira, D., Molinos, D., Moreira, R., & Silva, F. (2024). Evolved NWDAF towards a fully distributed artificial intelligence in the 6G network architecture. In Anais do IV Workshop de Redes 6G (pp. 15–25). SBC.
Nguyen, H. X., Trestian, R., To, D., & Tatipamula, M. (2021). Digital twin for 5G and beyond. IEEE Communications Magazine, 59(2), 10–15.
Ojo, S., Imoize, A., & Alienyi, D. (2021). Radial basis function neural network pathloss prediction model for LTE networks in multitransmitter signal propagation environments. International Journal of Communication Systems, 34(3), e4680.
Schölkopf, B. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press.
Singh, Y. (2012). Comparison of Okumura, Hata and COST-231 models on the basis of path loss and signal strength. International Journal of Computer Applications, 59.
Skocaj, M., et al. (2022). Cellular network capacity and coverage enhancement with MDT data and deep reinforcement learning. Computer Communications, 195, 403–415.
Tang, Z., et al. (2023). Multi-output Gaussian process-based data augmentation for multi-building and multi-floor indoor localization. arXiv:2202.01980 [cs].
Wang, M. (2018). Anomaly detection for mobile network management. 9(2), 19.
Wu, J., et al. (2021). Toward native artificial intelligence in 6G networks: System design, architectures, and paradigms. arXiv:2103.02823 [cs].
Zhou, T., & Peng, Y. (2023). Gaussian process regression based on deep neural network for reliability analysis in high dimensions. Structural and Multidisciplinary Optimization, 66(6), 131.
3GPP. (2024). 3rd generation partnership project; technical specification group radio access network; radio measurement collection for minimization of drive tests (MDT); overall description; stage 2. Technical Report TS 37.320, 3GPP.
Abbas, K., Khan, T. A., Afaq, M., & Song, W.-C. (2022). Ensemble learning-based network data analytics for network slice orchestration and management: An intent-based networking mechanism. In NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium (pp. 1–5). IEEE.
Carmack, J., et al. (2021). Neural network generative models for radio frequency data. In 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0577–0582). IEEE.
Cheerla, S., Ratnam, D. V., & Borra, H. S. (2018). Neural network-based path loss model for cellular mobile networks at 800 and 1800 MHz bands. AEU - International Journal of Electronics and Communications, 94, 179–186.
Deng, J., et al. (2021). A digital twin approach for self-optimization of mobile networks. In 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) (pp. 1–6). IEEE.
Dreifuerst, R. M., et al. (2021). Optimizing coverage and capacity in cellular networks using machine learning. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8138–8142). IEEE.
Gehlot, A., et al. (2022). Application of neural network in the prediction models of machine learning based design. In 2022 ICSES (pp. 1–6). IEEE.
Jeon, Y., Jeong, H., Seo, S., Kim, T., Ko, H., & Pack, S. (2022). A distributed NWDAF architecture for federated learning in 5G. In 2022 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1–2).
Kirkwood, C., Economou, T., Pugeault, N., & Odbert, H. (2022). Bayesian deep learning for spatial interpolation in the presence of auxiliary information. Mathematical Geosciences, 54(3), 507–531.
Neto, N. S., Gonçalves, M., Oliveira, D., Molinos, D., Moreira, R., & Silva, F. (2024). Evolved NWDAF towards a fully distributed artificial intelligence in the 6G network architecture. In Anais do IV Workshop de Redes 6G (pp. 15–25). SBC.
Nguyen, H. X., Trestian, R., To, D., & Tatipamula, M. (2021). Digital twin for 5G and beyond. IEEE Communications Magazine, 59(2), 10–15.
Ojo, S., Imoize, A., & Alienyi, D. (2021). Radial basis function neural network pathloss prediction model for LTE networks in multitransmitter signal propagation environments. International Journal of Communication Systems, 34(3), e4680.
Schölkopf, B. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press.
Singh, Y. (2012). Comparison of Okumura, Hata and COST-231 models on the basis of path loss and signal strength. International Journal of Computer Applications, 59.
Skocaj, M., et al. (2022). Cellular network capacity and coverage enhancement with MDT data and deep reinforcement learning. Computer Communications, 195, 403–415.
Tang, Z., et al. (2023). Multi-output Gaussian process-based data augmentation for multi-building and multi-floor indoor localization. arXiv:2202.01980 [cs].
Wang, M. (2018). Anomaly detection for mobile network management. 9(2), 19.
Wu, J., et al. (2021). Toward native artificial intelligence in 6G networks: System design, architectures, and paradigms. arXiv:2103.02823 [cs].
Zhou, T., & Peng, Y. (2023). Gaussian process regression based on deep neural network for reliability analysis in high dimensions. Structural and Multidisciplinary Optimization, 66(6), 131.
Publicado
19/05/2025
Como Citar
OLIVEIRA, Daniel Ricardo Cunha; MOREIRA, Rodrigo; SILVA, Flávio de Oliveira.
ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks. In: WORKSHOP DE REDES 6G (W6G), 5. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 41-48.
DOI: https://doi.org/10.5753/w6g.2025.9532.
