Decentralized Multi-Party Multi-Network AI for Global Deployment of 6G Wireless Systems

• URL: http://arxiv.org/abs/2407.01544v1
• Date: Mon, 15 Apr 2024 15:21:25 GMT
• Title: Decentralized Multi-Party Multi-Network AI for Global Deployment of 6G Wireless Systems
• Authors: Merim Dzaferagic, Marco Ruffini, Nina Slamnik-Krijestorac, Joao F. Santos, Johann Marquez-Barja, Christos Tranoris, Spyros Denazis, Thomas Kyriakakis, Panagiotis Karafotis, Luiz DaSilva, Shashi Raj Pandey, Junya Shiraishi, Petar Popovski, Soren Kejser Jensen, Christian Thomsen, Torben Bach Pedersen, Holger Claussen, Jinfeng Du, Gil Zussman, Tingjun Chen, Yiran Chen, Seshu Tirupathi, Ivan Seskar, Daniel Kilper,
• Abstract summary: 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. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks.
• Score: 31.754166695074353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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