面向分布式智能的AI原生6G：流量特性、感知与AI网格
AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid
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中文总结 AI 辅助
该研究针对AI原生6G的流量特性等问题，提出AI Grid分布式AI基础设施平台，结合AI感知连接使6G成为分布式智能平台。
中文摘要 AI 辅助
第六代（6G）移动网络的发展不仅将由人工智能（AI）赋能的网络自动化与优化塑造，还需将AI作为6G原生工作负载提供服务。新兴AI服务带来的流量与计算需求有别于传统移动宽带，其用户体验取决于有用信息的交付速度、多模态流的突发性与非对称性处理方式，以及推理、检索、缓存和内容处理的执行位置。本文从连接-计算联合视角研究AI原生6G：首先以上行/下行吞吐量偏度、突发性和令牌延迟为指标，表征代表性AI服务流量；接着探讨第五代扩展现实感知机制如何演进以适配6G中的AI流量特性感知需求；最后提出AI Grid作为分布式AI基础设施平台，可根据延迟、成本、策略和服务级别约束部署工作负载。综上，AI感知连接与AI Grid共同使6G成为分布式智能平台。
英文摘要
The sixth-generation (6G) of mobile networks will be shaped not only by artificial intelligence (AI)-enabled network automation and optimization, but also by the need to serve AI as a 6G-native workload. Emerging AI services introduce traffic and compute demands that differ from conventional mobile broadband. Their user experience depends on how quickly useful information is delivered, how bursty and asymmetric multimodal flows are handled, and where inference, retrieval, caching, and content processing are executed. This article presents a joint connectivity-compute view of AI-native 6G. We first characterize representative AI service traffic in terms of uplink/downlink throughput skew, burstiness, and token latency. Next, we discuss how fifth-generation extended reality awareness mechanisms can evolve toward AI traffic characteristics awareness in 6G. Finally, we introduce AI Grid as a distributed AI infrastructure platform for placing workloads according to latency, cost, policy, and service-level constraints. Together, AI-aware connectivity and AI Grid enable 6G as a distributed intelligence platform.
