OpenRAN Gym：一个在O-RAN中使用AI进行数据汇集和实验的开放式平台
OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN
摘要：
开放无线接入网络（RAN）架构将在下一代蜂窝网络中实现互操作性、开放性和编程数据驱动控制。然而，开发可扩展和高效的数据驱动算法，可以在不同的部署中推广并优化RAN性能，这是一项复杂的壮举，目前在很大程度上尚未解决。具体而言，为网络控制和推理设计高效的数据驱动算法的能力至少需要（i）访问大型、丰富和异构的数据集;（ii）在受控但现实的环境中进行大规模测试;以及（iii）自动化数据汇集和实验的软件管道。为了促进这些任务，在本文中，我们提出了OpenRAN Gym，一个实用的，开放的，实验性的工具箱，提供端到端的设计，数据汇集，并在下一代开放RAN系统的智能控制测试工作流程。OpenRAN Gym基于用于收集大型数据集和RAN控制的软件框架，以及用于实验无线平台的轻量级O-RAN环境。我们首先提供OpenRAN Gym的概述，然后描述它如何用于收集数据，设计和训练基于人工智能和机器学习的O-RAN应用程序（xApps），以及在软件化RAN上测试xApps。然后，我们提供了一个使用OpenRAN Gym设计的两个xApp的示例，用于控制在Colosseum测试床上部署的具有7个基站和42个用户的大规模网络。OpenRAN Gym及其软件组件是开源的，可供研究社区公开使用。
英文摘要：
Open Radio Access Network (RAN) architectures will enable interoperability, openness, and programmatic data-driven control in next generation cellular networks. However, developing scalable and efficient data-driven algorithms that can generalize across diverse deployments and optimize RAN performance is a complex feat, largely unaddressed as of today. Specifically, the ability to design efficient data-driven algorithms for network control and inference requires at a minimum (i) access to large, rich, and heterogeneous datasets; (ii) testing at scale in controlled but realistic environments, and (iii) software pipelines to automate data collection and experimentation. To facilitate these tasks, in this paper we propose OpenRAN Gym, a practical, open, experimental toolbox that provides end-to-end design, data collection, and testing workflows for intelligent control in next generation Open RAN systems. OpenRAN Gym builds on software frameworks for the collection of large datasets and RAN control, and on a lightweight O-RAN environment for experimental wireless platforms. We first provide an overview of OpenRAN Gym and then describe how it can be used to collect data, to design and train artificial intelligence and machine learning-based O-RAN applications (xApps), and to test xApps on a softwarized RAN. Then, we provide an example of two xApps designed with OpenRAN Gym and used to control a large-scale network with 7 base stations and 42 users deployed on the Colosseum testbed. OpenRAN Gym and its software components are open source and publicly-available to the research community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
