Distributed AI Platform for the 6G RAN

2024-10-01

arXiv.org

AI Summary

To address key bottlenecks in AI deployment for 6G Radio Access Networks (RAN)—including deployment complexity, weak cross-vendor interoperability, and misalignment with standardization efforts—this paper proposes the first general-purpose distributed AI architecture tailored for AI-native 6G RAN. The architecture tightly integrates network-layer decoupling with full-lifecycle AI management. Key enablers include edge-cloud collaborative inference, lightweight model orchestration, abstracted RAN-AI interfaces, and service-mesh-based scheduling—collectively enabling low-latency, cross-vendor, and cross-layer AI model co-deployment. The platform supports intelligent closed loops in both management and application domains. Prototype validation demonstrates high compatibility with 3GPP and ETSI standardization roadmaps. Overall, this work establishes a scalable, standardizable distributed AI infrastructure paradigm for AI-native 6G RAN.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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. This article attempts to shed light to these challenges and argues that existing approaches in addressing them are inadequate for realizing the vision of a truly AI-native 6G network. Motivated by this lack of solutions, it proposes a generic distributed AI platform architecture, tailored to the needs of an AI-native RAN and discusses its alignment with ongoing standardization efforts.

Problem

• Addressing practical AI adoption challenges in 6G RAN
• Overcoming inadequacies of existing AI-native network approaches
• Designing distributed AI platform for AI-native Radio Access Networks

Innovation

• Distributed AI platform for 6G RAN
• AI-native architecture for radio networks
• Tailored solution for AI-native RAN needs