OCUDU dApp 平台：用于实时AI-RAN的开放运行时与E3接口
The OCUDU dApp Platform: An Open Runtime and E3 Interface for Real-Time AI-RAN
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中文总结 AI 辅助
OCUDU dApp平台通过开放运行时和E3接口，在5G DU内以三类时序契约安全执行AI应用，实现无回退的实时运行，并公开预览以征集反馈。
中文摘要 AI 辅助
机器学习在3GPP新无线电（NR）5G分布式单元（DU）内低于10毫秒的频段中展现了其最大的收益：链路自适应、每时隙调度、信道估计以及接收机本身。目前尚无开放平台允许独立构建的软件在该频段运行。先前的dApp框架仅作为导出流的外部观察者触及该频段。本文是对OCUDU dApp平台的引导性介绍，该平台是一个开放运行时和E3接口，在此之下，经过签名的AI-RAN应用程序在三种时序契约下在生产级DU内执行：驻留在GPU接收链上（A类）、在调度器100微秒的准入截止时间内（B类）、或作为调度器消费其结果的不阻塞观察者（C类）。传统路径永不被取代，且每个权限都是类型化、验证且受运营商约束的。本文解释了运行时、嵌入式E3代理以及三个公共代码库如何协同工作；展示了一个dApp的源代码、其签名包及其生命周期状态机；定义了模块所依据的契约；并展示了单一管理面如何服务于Python脚本、运营商控制台和LLM代理。在带有连接手机的GB10 gNB上，所有三类dApp（包括一个树外神经均衡器）在一个实时小区上共同运行，无一次回退，且均衡器变体仅通过生命周期操作在空中进行了比较。每个测量的检查点都报告了其条件和差距。平台、SDK和零硬件快速入门在BSD-3-Clause-Clear许可下公开，作为OCUDU AI-RAN工作组2的预览版发布，欢迎在并入OCUDU主线之前提供反馈、新用例和独立验证。
英文摘要
Machine learning has shown its largest gains in the band below 10 ms inside a 3GPP new radio (NR) 5G distributed unit (DU): link adaptation, per-slot scheduling, channel estimation, and the receiver itself. No open platform has let independently built software run there. Prior dApp frameworks reached the band only as external observers of an export stream. This paper is a guided introduction to the OCUDU dApp platform, an open runtime and E3 interface under which signed AI-RAN applications execute inside a production DU under three timing contracts: resident on the GPU receive chain (Class A), inside the scheduler's 100 us admitted deadline (Class B), or as never-blocking observers whose results the scheduler consumes (Class C). The conventional path is never displaced, and every authority is typed, validated, and operator-bounded. The paper explains how the runtime, the embedded E3 agent, and the three public repositories fit together; shows a dApp's source, its signed package, and its lifecycle state machine; defines the contracts a module is written against; and shows how one management surface serves a Python script, an operator's console, and an LLM agent. On a GB10 gNB with attached handsets, dApps of all three classes, including an out-of-tree neural equalizer, ran together on a live cell without a single fallback, and equalizer variants were compared over the air by lifecycle operations alone. Every measured checkpoint is reported with its conditions and its gaps. Platform, SDK, and a zero-hardware quickstart are public under BSD-3-Clause-Clear as a preview release of the OCUDU AI-RAN Working Group 2, inviting feedback, new use cases, and independent vetting ahead of upstreaming into the OCUDU mainline.
发表机构
- DeepSig Inc.（DeepSig公司）
机构由 AI 辅助整理，请以论文原文为准。
