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

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arxiv 2501.09007 v1 pith:3772OMJK submitted 2025-01-15 cs.AI cs.NIeess.SP

Lopamudra Kundu , Xingqin Lin , Rajesh Gadiyar , Jean-Francois Lacasse , Shuvo Chowdhury This is my paper · ORCID
classification cs.AIcs.NIeess.SP
keywords ai-ranarticlecomputingfutureinfrastructuretowardsworkloadsaccess
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AI-RAN on NPUs: Baseband Processing Without Baseband Chips

    eess.SP 2026-07 accept novelty 7.0 of 10

    A complete OFDM transceiver runs end-to-end over the air on a commercial edge NPU by remapping baseband operators onto dense matrix and vector engines.

  2. Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control

    eess.SY 2026-01 conditional novelty 6.0 of 10

    A single GPU edge node, split into isolated hardware partitions, runs both 5G radio and a vision-language model and closes the drone control loop in 500-680 ms.

  3. Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization

    cs.NI 2025-07 conditional novelty 5.0 of 10

    A large telecom operator proposes a 6G RAN architecture with a centralized AI Node as a Day 1 standard feature, backed by a 5,000-site field trial reporting latency, energy, and diagnostic gains.

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