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

TL;DR

This paper proposes a decentralized AI framework for 6G networks that integrates radio and optical domains, enabling autonomous, scalable, and secure network management at the edge.

Contribution

It introduces the DMMAI framework for multi-network AI control, addressing the integration of radio and optical domains in 6G networks at scale.

Findings

Framework supports autonomous network configuration and repair

Addresses multi-domain AI coordination for 6G

Lays groundwork for global AI standards in 6G

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.