AI-RAN: Transforming RAN with AI-driven Computing Infrastructure
This addresses the need for more efficient and capable networks in telecommunications, but it is incremental as it builds on existing convergence trends.
The paper tackles the problem of transforming radio access networks (RAN) by integrating AI workloads on the same infrastructure, proposing AI-RAN to improve performance and asset utilization, with a proof-of-concept demonstrated using NVIDIA Grace-Hopper GH200 servers.
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.