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　　两个最快的控制回路（紫色和橙色）与传统的 RRM 有关。示例包括紫色（第 1 层和第 2 层）控制环路中的调度和链路自适应以及橙色（第 3 层）控制环路中的承载管理和切换。这些控制回路中的功能在相当长一段时间内已经是自主的，例如，在从毫秒 (ms) 到几百毫秒的时间范围内，基于内部数据进行调度和切换的决策。从架构的角度来看，这些控制回路是在图 3 所示的 RAN 网络功能域中实现的。

　　图 3 左侧显示的较慢控制回路代表网络设计（深绿色）和网络优化和保障（浅绿色）。与两个快速控制循环相比，这些较慢的循环目前在很大程度上是手动的。网络设计涵盖与整个 RAN 的设计和部署相关的活动，而网络自动化涵盖对已部署功能的观察和优化。网络优化和保证是通过观察某个功能的性能并更改暴露的配置参数以改变已部署功能的行为来完成的，从而确保其在已部署的特定环境中的意图。从架构的角度来看，这些控制回路是在 RAN 自动化应用领域 [7] 中实现的。

　　绿色控制循环包含大部分手动工作，这些手动工作将因 RAN 自动化而消失，这解释了为什么 AI/ML 已经在这些循环中实施 [8]。然而，将 RAN 自动化解决方案仅限于绿色控制回路是错误的。AI/ML 还可以增强紫色和橙色控制回路中的功能，使其在不同环境中的部署更具适应性和鲁棒性。这反过来又限度地减少了浅绿色控制回路中所需的配置优化量。

　　虽然图 3 中的控制回路都在 RAN 域内部，但强大的 RAN 自动化解决方案中的一些功能将依赖于其他域的资源。该功能将作为 RAN 自动化应用程序域的一部分来实现。RAN自动化平台域将提供跨域交互所需的服务。

　　RAN 自动化应用领域中 RAN 自动化功能的一个示例是 ERAN 的自动化部署和配置。在 ERAN 部署中，AI/ML 用于集群共享无线电覆盖范围的基带，因此应配置为协调功能，例如调度 [8]。为此，需要对来自多个网络功能的数据进行聚类，以了解它们中的哪些共享无线电覆盖范围。此过程需要拓扑和清单信息，这些信息将通过网络自动化平台在 R1 上公开的服务提供给 rApp。

　　聚类结果的结果是应该协调的基带配置以及来自传输域的资源请求。此信息也可以通过传输自动化应用程序提供的服务获取，这些应用程序通过 R1 框架公开服务。在为集群设计 rApp 时，有必要详细了解 RAN 网络功能中协调功能的实现，以了解应如何在 rApp 中执行集群分析。

　　网络功能域中 RAN 自动化功能的一个示例是基于 AI/ML 的链路自适应，其中基于 AI/ML 的功能优化调制和编码方案的选择，以实现吞吐量或最小延迟，消除误块率目标参数，从而需要基于配置的优化。另一个例子是辅助载波预测 [8]，其中 AI/ML 用于学习特定部署的不同载波之间的覆盖关系。这两个示例都使用网络功能内部的数据。

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　　The left side of Figure 3 illustrates how the task of efficiently operating a RAN to best utilize the deployed resources (base stations or frequencies) can be divided into different control loops acting according to different time scales and with different scopes. A successful RAN automation solution will require the use of AI/ML technologies [6] in all of these control loops to ensure functionality that can work autonomously in different deployments and environments in an optimal way.

　　The two fastest control loops (purple and orange) are related to traditional RRM. Examples include scheduling and link adaptation in the purple (layer 1 and 2) control loop and bearer management and handover in the orange (layer 3) control loop. Functionality in these control loops has already been autonomous for quite some time, with the decision-making based on internal data for scheduling and handover in a timeframe ranging from milliseconds (ms) to several hundred ms, for example. From an architecture perspective, these control loops are implemented in the RAN network function domain shown in Figure 3.

　　The slower control loops shown on the left side of Figure 3 represent network design (dark green) and network optimization and assurance (light green). In contrast to the two fast control loops, these slower loops are to a large degree manual at present. Network design covers activities related to the design and deployment of the full RAN, while network automation covers observation and optimization of the deployed functionality. Network optimization and assurance is done by observing the performance of a certain functionality and changing the exposed configuration parameters to alter the behavior of the deployed functionality, so that it assures the intents in the specific environment where it has been deployed. From an architecture perspective, these control loops are implemented in the RAN automation application domain [7].

　　The green control loops encompass the bulk of the manual work that will disappear as a result of RAN automation, which explains why AI/ML is already being implemented in those loops [8]. It would, however, be a mistake to restrict the RAN automation solution to just the green control loops. AI/ML also makes it possible to enhance the functionality in the purple and orange control loops to make them more adaptive and robust for deployment in different environments. This, in turn, minimizes the amount of configuration optimization that is needed in the light-green control loop.

　　While the control loops in Figure 3 are all internal to the RAN domain, some of the functionality in a robust RAN automation solution will depend on resources from other domains. That functionality would be implemented as part of the RAN automation application domain. The RAN automation platform domain will provide the services required for cross-domain interaction.

　　One example of RAN automation functionality in the RAN automation application domain is the automated deployment and configuration of ERAN. In ERAN deployments, AI/ML is used to cluster basebands that share radio coverage and therefore should be configured to coordinate functionality such as scheduling [8]. To do this, data from several network functions needs to be clustered to understand which of them share radio coverage. This process requires topology and inventory information that will be made available to the rApps through the services exposed by the network automation platform over R1.

　　The outcome of the clustering results is a configuration of the basebands that should coordinate as well as a request for resources from the transport domain. This information can also be obtained by services provided by transport automation applications exposing services through the R1 framework. When designing the rApp for clustering, it is beneficial to have detailed knowledge about the implementation of coordination functionality in the RAN network function to understand how the clustering analysis in the rApp should be performed.

　　An example of RAN automation functionality in the network function domain is AI/ML-based link adaptation, where AI/ML-based functionality optimizes the selection of the modulation and coding scheme for either maximum throughput or minimum delay, removing the block error rate target parameter and thereby the need for configuration-based optimization. Another example is secondary carrier prediction [8], where AI/ML is used to learn coverage relations between different carriers for a certain deployment. Both of these examples use data that is internal to the network function.