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

Lopamudra Kundu, Xingqin Lin, Rajesh Gadiyar, Jean-Francois Lacasse, Shuvo Chowdhury
1/15/2025
cs.AIcs.NIeess.SP

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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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Cite this paper

@article{kundu2025airan,
  title  = {AI-RAN: Transforming RAN with AI-driven Computing Infrastructure},
  author = {Lopamudra Kundu and Xingqin Lin and Rajesh Gadiyar and Jean-Francois Lacasse and Shuvo Chowdhury},
  year   = {2025},
  eprint = {2501.09007},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.09007v1}
}

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