AI-RAN: Transforming RAN with AI-driven Computing Infrastructure
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arXiv
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| Format: | Preprint |
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2025
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| author | Kundu, Lopamudra Lin, Xingqin Gadiyar, Rajesh Lacasse, Jean-Francois Chowdhury, Shuvo |
| author_facet | Kundu, Lopamudra Lin, Xingqin Gadiyar, Rajesh Lacasse, Jean-Francois Chowdhury, Shuvo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-RAN: Transforming RAN with AI-driven Computing Infrastructure Kundu, Lopamudra Lin, Xingqin Gadiyar, Rajesh Lacasse, Jean-Francois Chowdhury, Shuvo Artificial Intelligence Networking and Internet Architecture Signal Processing 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. |
| title | AI-RAN: Transforming RAN with AI-driven Computing Infrastructure |
| topic | Artificial Intelligence Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2501.09007 |