Recent years have witnessed the Open Radio Access Network (RAN) paradigm transforming the fundamental ways cellular systems are deployed, managed, and optimized. This shift is led by concepts such as openness, softwarization, programmability, interoperability, and intelligence of the network, all of which had never been applied to the cellular ecosystem before. The realization of the Open RAN vision into practical architectures, intelligent data-driven control loops, and efficient software implementations, however, is a multifaceted challenge, which requires (i) datasets to train Artificial Intelligence (AI) and Machine Learning (ML) models; (ii) facilities to test models without disrupting production networks; (iii) continuous and automated validation of the RAN software; and (iv) significant testing and integration efforts. This paper poses itself as a tutorial on how Colosseum - the world's largest wireless network emulator with hardware in the loop - can provide the research infrastructure and tools to fill the gap between the Open RAN vision, and the deployment and commercialization of open and programmable networks. We describe how Colosseum implements an Open RAN digital twin through a high-fidelity Radio Frequency (RF) channel emulator and end-to-end softwarized O-RAN and 5G-compliant protocol stacks, thus allowing users to reproduce and experiment upon topologies representative of real-world cellular deployments. Then, we detail the twinning infrastructure of Colosseum, as well as the automation pipelines for RF and protocol stack twinning. Finally, we showcase a broad range of Open RAN use cases implemented on Colosseum, including the real-time connection between the digital twin and real-world networks, and the development, prototyping, and testing of AI/ML solutions for Open RAN.

翻译：近年来，开放式无线接入网（RAN）范式正在从根本上改变蜂窝系统的部署、管理和优化方式。这一变革由网络开放性、软件化、可编程性、互操作性和智能化等理念引领——这些概念此前从未被应用于蜂窝生态系统。然而，将开放式无线接入网的愿景转化为实际架构、智能数据驱动控制环路及高效软件实现是一项多维度挑战，需要：（i）用于训练人工智能与机器学习模型的数据集；（ii）在不中断生产网络的前提下测试模型的设施；（iii）对RAN软件进行持续自动化验证；（iv）大量测试与集成工作。本文作为技术教程，阐述全球最大的硬件在环无线网络仿真器Colosseum如何提供研究基础设施与工具，以弥合开放式RAN愿景与开放可编程网络部署商业化之间的差距。我们描述了Colosseum如何通过高保真射频信道仿真器及端到端软化的O-RAN与5G协议栈实现开放式RAN数字孪生，使用户能够复现真实蜂窝网络部署拓扑并开展实验。随后，我们详细介绍了Colosseum的孪生基础设施，以及射频与协议栈孪生的自动化流水线。最后，我们展示了Colosseum上实现的开放式RAN广泛用例，包括数字孪生与真实网络的实时连接，以及面向开放式RAN的人工智能/机器学习解决方案的开发、原型设计与测试。