面向开放和可编程AI-RAN的成本建模与收益机会的技术经济框架
A Techno-Economic Framework for Cost Modeling and Revenue Opportunities in Open and Programmable AI-RAN
AI总结：
本文提出一种技术经济分析框架，结合5G层1处理基准和真实流量模型，评估AI-RAN部署的计算能力与收益，证明GPU密集型部署的回报率可达8倍。
摘要：
大规模部署5G网络未能达到预期投资回报，引发对6G经济可行性的担忧。同时，AI推理和训练需求激增，全球计算能力面临压力。AI-RAN架构通过在非高峰时段利用GPU闲置容量与AI工作负载共享，提供改进的资本效率路径。然而，此类系统的经济案例尚未得到证实。本文通过结合公开可用的5G层1处理基准、异构平台性能数据、真实流量模型和AI服务需求特征，构建了一个联合成本和收益模型，量化GPU基于RAN部署的额外计算能力，并评估将其租赁给AI租户的收益。研究结果表明，在涵盖令牌贬值、需求动态变化和多样化GPU服务密度的多种场景中，GPU密集型部署的额外资本和运营支出被AI-RAN收益抵消，投资回报率可达最高8倍。这些发现强化了基于加速器的RAN架构和未来6G部署的长期经济案例。
英文摘要：
The large-scale deployment of 5G networks has not delivered the expected return on investment for mobile network operators, raising concerns about the economic viability of future 6G rollouts. At the same time, surging demand for Artificial Intelligence (AI) inference and training workloads is straining global compute capacity. AI-RAN architectures, in which Radio Access Network (RAN) platforms accelerated on Graphics Processing Unit (GPU) share idle capacity with AI workloads during off-peak periods, offer a potential path to improved capital efficiency. However, the economic case for such systems remains unsubstantiated. In this paper, we present a techno-economic analysis of AI-RAN deployments by combining publicly available benchmarks of 5G Layer-1 processing on heterogeneous platforms -- from x86 servers with accelerators for channel coding to modern GPUs -- with realistic traffic models and AI service demand profiles for Large Language Model (LLM) inference. We construct a joint cost and revenue model that quantifies the surplus compute capacity available in GPU-based RAN deployments and evaluates the returns from leasing it to AI tenants. Our results show that, across a range of scenarios encompassing token depreciation, varying demand dynamics, and diverse GPU serving densities, the additional capital and operational expenditures of GPU-heavy deployments are offset by AI-on-RAN revenue, yielding a return on investment of up to 8x. These findings strengthen the long-term economic case for accelerator-based RAN architectures and future 6G deployments.
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