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

• URL: http://arxiv.org/abs/2501.09007v1
• Date: Wed, 15 Jan 2025 18:47:05 GMT
• Title: AI-RAN: Transforming RAN with AI-driven Computing Infrastructure
• Authors: Lopamudra Kundu, Xingqin Lin, Rajesh Gadiyar, Jean-Francois Lacasse, Shuvo Chowdhury,
• Abstract summary: This article introduces AI-RAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure.<n>By doing so, AI-RAN not only meets the performance demands of future networks but also improves asset utilization.
• Score: 1.3970999841029235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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