AI-RAN: Transforming RAN with AI-driven Computing Infrastructure

TL;DR

AI-RAN proposes a unified infrastructure that integrates RAN and AI workloads, enhancing performance and asset utilization, with a practical proof-of-concept demonstrating concurrent processing on advanced servers.

Contribution

The paper introduces the concept of AI-RAN, detailing its architecture, key requirements, and a practical implementation using NVIDIA servers, advancing the convergence of communication and AI workloads.

Findings

Successful concurrent processing of RAN and AI workloads

Enhanced asset utilization through integrated infrastructure

Practical demonstration on NVIDIA Grace-Hopper GH200 servers

Abstract

The radio access network (RAN) landscape is undergoing a transformative shift from traditional, communication-centric infrastructures towards converged compute-communication platforms. This article introduces AI-RAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure. By doing so, AI-RAN not only meets the performance demands of future networks but also improves asset utilization. We begin by examining how RANs have evolved beyond mobile broadband towards AI-RAN and articulating manifestations of AI-RAN into three forms: AI-for-RAN, AI-on-RAN, and AI-and-RAN. Next, we identify the key requirements and enablers for the convergence of communication and computing in AI-RAN. We then provide a reference architecture for advancing AI-RAN from concept to practice. To illustrate the practical potential of AI-RAN, we present a proof-of-concept that concurrently processes RAN and AI workloads utilizing NVIDIA Grace-Hopper GH200 servers. Finally, we conclude the article by outlining future work directions to guide further developments of AI-RAN.