ColO-RAN: Developing Machine Learning-Based xApps for Open RAN Closed-Loop Control on Programmable Experimental Platforms
ColO-RAN: Developing Machine Learning-Based xApps for Open RAN Closed-Loop Control on Programmable Experimental Platforms
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DOI：
10.1109/tmc.2022.3188013
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发表时间：
2021-12
影响因子：
7.9
通讯作者：
Michele Polese;Leonardo Bonati;Salvatore D’oro;S. Basagni;T. Melodia
中科院分区：
文献类型：
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作者：
Michele Polese;Leonardo Bonati;Salvatore D’oro;S. Basagni;T. Melodia
蜂窝网络正在经历一场彻底的变革，朝着分解、完全虚拟化和可编程的架构转变，异构设备和应用程序日益增多。在此背景下，O-RAN 联盟标准化的开放架构通过闭环控制实现算法和独立于硬件的无线接入网络 (RAN) 适配。 O-RAN 引入了基于机器学习 (ML) 的网络控制和自动化算法，即在 RAN 智能控制器上运行的所谓 xApp。然而，尽管 Open RAN 带来了新机遇，但基于 ML 的网络自动化进展缓慢，主要是因为缺乏大规模数据集和实验测试基础设施。这减缓了深度强化学习 (DRL) 代理在实际网络上的开发和广泛采用，从而延迟了智能和自主 RAN 控制的进展。在本文中，我们通过讨论 Open RAN 中基于 DRL 的闭环控制的设计、训练、测试和实验评估的见解和实用解决方案来应对这些挑战。为此，我们推出了 ColO-RAN，这是第一个公开可用的具有软件定义无线电在环的大规模 O-RAN 测试框架。 ColO-RAN 以 Colosseum 无线网络模拟器的规模和计算能力为基础，利用 O-RAN 组件、可编程基站和“无线数据工厂”实现大规模机器学习研究。具体来说，我们设计和开发了三个示例性 xApp，用于基于 DRL 的 RAN 切片、调度和在线模型训练控制，并评估它们在具有 7 个软件化基站和 42 个用户的蜂窝网络上的性能。最后，我们通过将 ColO-RAN 部署在室内可编程测试平台 Arena 上，展示了 ColO-RAN 到不同平台的可移植性。从 ColO-RAN 实施中汲取的经验教训以及我们首次大规模评估的广泛结果凸显了实验框架对于开发端到端智能 RAN 控制管道（从数据分析到 DRL 代理的设计和测试）的重要性。他们还提供了有关基于 DRL 的自适应控制的挑战和优势的见解，以及与实时 RAN 训练相关的权衡。 ColO-RAN 和收集的大规模数据集可供研究界公开使用。
Cellular networks are undergoing a radical transformation toward disaggregated, fully virtualized, and programmable architectures with increasingly heterogeneous devices and applications. In this context, the open architecture standardized by the O-RAN Alliance enables algorithmic and hardware-independent Radio Access Network (RAN) adaptation through closed-loop control. O-RAN introduces Machine Learning (ML)-based network control and automation algorithms as so-called xApps running on RAN Intelligent Controllers . However, in spite of the new opportunities brought about by the Open RAN, advances in ML-based network automation have been slow, mainly because of the unavailability of large-scale datasets and experimental testing infrastructure. This slows down the development and widespread adoption of Deep Reinforcement Learning (DRL) agents on real networks, delaying progress in intelligent and autonomous RAN control. In this paper, we address these challenges by discussing insights and practical solutions for the design, training, testing, and experimental evaluation of DRL-based closed-loop control in the Open RAN. To this end, we introduce ColO-RAN, the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. Building on the scale and computational capabilities of the Colosseum wireless network emulator, ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a “wireless data factory.” Specifically, we design and develop three exemplary xApps for DRL-based control of RAN slicing, scheduling and online model training, and evaluate their performance on a cellular network with 7 softwarized base stations and 42 users. Finally, we showcase the portability of ColO-RAN to different platforms by deploying it on Arena, an indoor programmable testbed. The lessons learned from the ColO-RAN implementation and the extensive results from our first-of-its-kind large-scale evaluation highlight the importance of experimental frameworks for the development of end-to-end intelligent RAN control pipelines, from data analysis to the design and testing of DRL agents. They also provide insights on the challenges and benefits of DRL-based adaptive control, and on the trade-offs associated to training on a live RAN. ColO-RAN and the collected large-scale dataset are publicly available to the research community.