将AI与无线网络融合，提升算力利用率和网络性能。
AI-RAN: Transforming RAN with AI-driven Computing Infrastructure
- 在同个平台同时运行通信与AI任务，实现算力与网络协同。
- 通过实测验证：单服务器可并发处理基站与AI计算负载。
- 适合研究网络智能化、边缘计算的开发者与工程师。
无线接入网（RAN）正从传统以通信为中心的架构，转向融合计算与通信的新型平台。本文提出AI-RAN，将RAN与人工智能（AI）工作负载统一部署于同一基础设施上。该设计不仅满足未来网络的性能需求，还显著提升硬件资产利用率。文章首先梳理了RAN从移动宽带向AI-RAN演进的路径，并提炼出三种典型形态：AI-for-RAN（AI辅助网络）、AI-on-RAN（AI运行于网络）、AI-and-RAN（AI与网络深度融合）。随后，识别出实现通信与计算融合的关键需求与使能技术。进一步提出一个参考架构，推动AI-RAN从概念走向实践。为验证其可行性，文中展示了一个原型系统，基于NVIDIA Grace-Hopper GH200服务器，成功实现RAN与AI任务的并行处理。最后，文章展望未来研究方向，为持续发展提供指引。
原文摘要 · Abstract (English)
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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