Export Publication

Moura, J. & Santana, P. (2025). Toward End-to-End Deep Learning for Autonomous Management in Next-Generation Networks. In Proceedings for the 16th International Conference on Ubiquitous and Future Networks. (pp. 1-6). Lisboa: 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, IEEE, 2025, pp. 1-6

@inproceedings{moura2025_1764926844646,
	author = "Moura, J. and Santana, P.",
	title = "Toward End-to-End Deep Learning for Autonomous Management in Next-Generation Networks",
	booktitle = "Proceedings for the 16th International Conference on Ubiquitous and Future Networks",
	year = "2025",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/ICUFN65838.2025",
	pages = "1-6",
	publisher = "IEEE",
	address = "Lisboa",
	organization = "",
	url = "https://icufn.org/"
}

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
SP  - 1-6
SN  - 2165-8528
DO  - 10.1109/ICUFN65838.2025
CY  - Lisboa
UR  - https://icufn.org/
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  -