TY - GEN
T1 - Decentralized AI-Control Framework for Multi-Party Multi-Network 6G Deployments
AU - Dzaferagic, Merim
AU - Ruffini, Marco
AU - Slamnik-Krijestorac, Nina
AU - Santos, Joao F.
AU - Marquez-Barja, Johann
AU - Tranoris, Christos
AU - Denazis, Spyros
AU - Tziavas, Georgios Christos
AU - Kyriakakis, Thomas
AU - Karafotis, Panagiotis
AU - Dasilva, Luiz
AU - Pandey, Shashi Raj
AU - Shiraishi, Junya
AU - Popovski, Petar
AU - Jensen, Soren Kejser
AU - Thomsen, Christian
AU - Pedersen, Torben Bach
AU - Claussen, Holger
AU - Du, Jinfeng
AU - Zussman, Gil
AU - Chen, Tingjun
AU - Chen, Yiran
AU - Tirupathi, Seshu
AU - Seskar, Ivan
AU - Kilper, Daniel
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.
AB - Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.
UR - https://www.scopus.com/pages/publications/105018065272
U2 - 10.1109/ICCWorkshops67674.2025.11162408
DO - 10.1109/ICCWorkshops67674.2025.11162408
M3 - Conference proceeding
AN - SCOPUS:105018065272
T3 - 2025 IEEE International Conference on Communications Workshops, ICC Workshops 2025
SP - 1227
EP - 1232
BT - 2025 IEEE International Conference on Communications Workshops, ICC Workshops 2025
A2 - Valenti, Matthew
A2 - Reed, David
A2 - Torres, Melissa
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Communications Workshops, ICC Workshops 2025
Y2 - 8 June 2025 through 12 June 2025
ER -