Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Moura, J. & Santana, P. (2025). Toward End-to-End Deep Learning for Autonomous Management in Next-Generation Networks. Proceedings for the 16th International Conference on Ubiquitous and Future Networks.
Exportar Referência (IEEE) 
J. A. Moura and P. F. Santana,  "Toward End-to-End Deep Learning for Autonomous Management in Next-Generation Networks", in Proc. for the 16th Int. Conf. on Ubiquitous and Future Networks, Lisboa, 2025
Exportar BibTeX 
@misc{moura2025_1764929728267,
	author = "Moura, J. and Santana, P.",
	title = "Toward End-to-End Deep Learning for Autonomous Management in Next-Generation Networks",
	year = "2025",
	howpublished = "Digital"
}
Exportar RIS 
TY  - CPAPER
TI  - Toward End-to-End Deep Learning for Autonomous Management in Next-Generation Networks
T2  - Proceedings for the 16th International Conference on Ubiquitous and Future Networks
AU  - Moura, J.
AU  - Santana, P.
PY  - 2025
CY  - Lisboa
AB  - The evolution towards next-generation of mobile networks demands for autonomous network management, emphasizing data-driven solutions based on Artificial Intelligence (AI), in particular machine learning. To attain such goals, this paper proposes a hybrid end-to-end learning approach that integrates imitation learning, deep reinforcement learning, simulation, domain adaptation, multi-agent cooperation, explainable AI, and generative AI. The work outlines a comprehensive vision for online agent learning about optimum network management policies while ensuring safety, interpretability, and adaptability in highly complex and dynamic use cases at the network periphery.
ER  -