Distributed AI Platform for the 6G RAN

TL;DR

This paper proposes a distributed AI platform architecture designed specifically for the evolving 6G Radio Access Network, aiming to overcome practical challenges and enable AI-native functionalities in future cellular networks.

Contribution

It introduces a novel distributed AI platform architecture tailored for 6G RAN, addressing current limitations and supporting AI-driven network management and applications.

Findings

Identifies key challenges in deploying AI in 6G RANs

Proposes a new distributed AI platform architecture

Lays groundwork for AI-native 6G networks

Abstract

Cellular Radio Access Networks (RANs) are rapidly evolving towards 6G, driven by the need to reduce costs and introduce new revenue streams for operators and enterprises. In this context, AI emerges as a key enabler in solving complex RAN problems spanning both the management and application domains. Unfortunately, and despite the undeniable promise of AI, several practical challenges still remain, hindering the widespread adoption of AI applications in the RAN space. In this work, we attempt to shed light to these challenges and argue that existing approaches in addressing them are inadequate for realizing the vision of a truly AI-native 6G network. We propose a distributed AI platform architecture, tailored to the needs of an AI-native RAN.