AI-RAN: Transforming RAN with AI-driven Computing Infrastructure
Lopamudra Kundu, Xingqin Lin, Rajesh Gadiyar, Jean-Francois Lacasse,, and Shuvo Chowdhury

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…
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Taxonomy
TopicsBrain Tumor Detection and Classification · Robotics and Automated Systems · Internet of Things and AI
