Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G

TL;DR

This paper proposes a novel open architecture for 6G networks that integrates AI workloads with RAN infrastructure, enabling flexible, standardized, and multi-vendor compatible orchestration of telecommunications and AI at the network edge.

Contribution

It introduces a converged O-RAN and AI-RAN architecture with innovative orchestrator and distributed edge AI platforms for unified management of network and AI workloads.

Findings

Supports heterogeneous AI deployments at the network edge

Enables integrated resource management across RAN and AI workloads

Maintains open, standardized interfaces for multi-vendor interoperability

Abstract

Data-intensive Artificial Intelligence (AI) applications at the network edge demand a fundamental shift in Radio Access Network (RAN) design, from merely consuming AI for network optimization, to actively enabling distributed AI workloads. This presents a significant opportunity for network operators to monetize AI while leveraging existing infrastructure. To realize this vision, this article presents a novel converged O-RAN and AI-RAN architecture for unified orchestration and management of telecommunications and AI workloads on shared infrastructure. The proposed architecture extends the Open RAN principles of modularity, disaggregation, and cloud-nativeness to support heterogeneous AI deployments. We introduce two key architectural innovations: (i) the AI-RAN Orchestrator, which extends the O-RAN Service Management and Orchestration (SMO) to enable integrated resource and allocation across RAN and AI workloads; and (ii) AI-RAN sites that provide distributed edge AI platforms with real-time processing capabilities. The proposed architecture enables flexible orchestration, meeting requirements for managing heterogeneous workloads at different time scales while maintaining open, standardized interfaces and multi-vendor interoperability.