GJR2390200R1211 83SR04 ABB继电器模块

　　RAN 网络功能域（图 3 中的浅紫色框）包含 3GPP 定义的 RAN 网络功能 [5] 和无线电资源管理 (RRM) 功能等。它使用标准化和开放的 O1 和 A1 接口 [4] 与直接位于其之上的网络自动化平台域进行通信。A1 和 O1 的开放性将允许第三方自动化平台提供商。为了更好地支持创新和开放，ORAN 还在标准化扩展 O1、A1 和 R1 接口的方法，以实现具有竞争力的生态系统并加快新功能的上市时间。

　　RAN网络功能域通过R1应用程序编程接口（API）向更高层提供数据收集和分发服务以及自动化支持服务。此类自动化服务的示例是数据管理、库存和拓扑以及 RAN 自动化应用程序域（称为 rApps）中软件生命周期管理 (LCM) 的服务。

　　RAN 自动化应用领域（图 3 中的深紫色框）包括一些用于实现不同 RAN 自动化用例的智能。与 O-RAN 术语一致，这种智能是在 rApp 与网络自动化平台的帮助下实现的，目的是使用 R1 接口优化底层网络功能的性能。R1 接口的开放性提供了对 O1、O2 和 A1 相关服务的访问，例如，将允许第三方提供商开发 rApp。由于网络功能中对 RAN 功能的依赖性，闭环自动化通常最适合 RAN 供应商的 rApp。

　　RAN 自动化应用程序域和网络自动化平台通过 ARMI 从 E2E 管理和编排域（图 3 中的橙色框）接收 RAN 意图，ARMI 指导 RAN 自动化功能的操作。

　　图 3 的底部显示了为 RAN 自动化解决方案提供资源的域。对于某些功能（例如 Elastic RAN (ERAN)），RAN 自动化解决方案将通过 R1 上的传输自动化应用程序公开的服务从传输域（浅灰色）请求资源。对于云 RAN 实施，基础设施域（深灰色）将是必不可少的，因为这将为要在其上执行的 RAN 功能提供计算、存储和本地网络资源。当 RAN 自动化解决方案需要来自该域的资源时，它将使用 O2 接口。

　　图 3 的右侧显示了网络供应商的 DDD 域（深绿色）。该域通过支持 CI/CD（持续集成和持续交付）流程以及从现网获取系统反馈到研发过程中，与部署在网络域和 RAN 自动化应用程序域中的 RAN 软件进行交互。DDD 领域有一个数据科学环境，包括 AI/ML 训练基础设施。该环境支持新 ML 模型的设计、构建、培训、测试和部署，用于支持网络供应商的产品供应。

　　该架构的基础但未在图中明确显示是通过使用数据管道 [6] 在域内和域之间有效处理数据。

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　　The RAN network function domain (the light-purple box in Figure 3) contains the 3GPP-defined RAN network functions [5] and the Radio Resource Management (RRM) functionality, among others. It uses the standardized and open O1 and A1 interfaces [4] to communicate with the network automation platform domain that is directly above it. The openness of A1 and O1 will allow for third-party automation platform providers. To better support both innovation and openness, ORAN is also standardizing the means of extending the O1, A1 and R1 interfaces to enable a competitive ecosystem and quick time to market of new functionality.

　　The RAN network function domain provides data collection and distribution services as well as automation support services to higher layers through the R1 application programming interface (API). Examples of such automation services are data management, inventory and topology and services for life-cycle management (LCM) of software in the RAN automation application domain (known as rApps).

　　The RAN automation applications domain (the dark-purple box in Figure 3) includes some of the intelligence that is used to realize different RAN automation use cases. Consistent with O-RAN terminology, this intelligence is realized with the help of rApps working together with the network automation platform with the objective to optimize the performance of underlying network functions using the R1 interface. The openness of the R1 interface, which provides access to O1, O2 and A1 related services, for example, will allow for the development of rApps from third-party providers. Due to dependencies on RAN features within the network function, closed-loop automation will often work best with rApps from the RAN vendor.

　　The RAN automation applications domain and network automation platform receive RAN intents from the E2E management and orchestration domain (the orange box in Figure 3) through the ARMI, which guides the actions of the RAN automation functionality.

　　The bottom of Figure 3 shows the domains that provide resources to the RAN automation solution. For some features – such as Elastic RAN (ERAN) – the RAN automation solution will request resources from the transport domain (light grey) through services exposed by transport automation applications over R1. For a cloud RAN implementation, the infrastructure domain (dark grey) will be essential, as this will provide the compute, storage and local networking resources for the RAN functions on which to execute. When the RAN automation solution requires resources from this domain, it will use the O2 interface.

　　The right side of Figure 3 illustrates the network vendor’s DDD domain (dark green). This domain interacts with the RAN software deployed in the network domain and the RAN automation applications domain by supporting the CI/CD (continuous integration and continuous delivery) flow as well as getting system feedback from live networks into the R&D process. The DDD domain has a data science environment, including AI/ML training infrastructure. This environment enables the design, build, training, testing and deployment of new ML models, used to support the network vendor’s product offering.

　　Fundamental to the architecture but not explicitly shown in the figure is the efficient handling of data within and between the domains through the use of data pipelines [6].