Next generation (NextG) cellular networks will be natively cloud-based and built on programmable, virtualized, and disaggregated architectures. The separation of control functions from the hardware fabric and the introduction of standardized control interfaces will enable the definition of custom closed-control loops, which will ultimately enable embedded intelligence and real-time analytics, thus effectively realizing the vision of autonomous and self-optimizing networks. This article explores the disaggregated network architecture proposed by the O-RAN Alliance as a key enabler of NextG networks. Within this architectural context, we discuss the potential, the challenges, and the limitations of data-driven optimization approaches to network control over different timescales. We also present the first large-scale integration of O-RAN-compliant software components with an open source full-stack softwarized cellular network. Experiments conducted on Colosseum, the world's largest wireless network emulator, demonstrate closed-loop integration of real-time analytics and control through deep reinforcement learning agents. We also show the feasibility of radio access network (RAN) control through xApps running on the near-real-time RAN intelligent controller to optimize the scheduling policies of coexisting network slices, leveraging the O-RAN open interfaces to collect data at the edge of the network.

中文翻译：

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用于数据驱动的 NextG 蜂窝网络的 O-RAN 智能和学习


下一代 (NextG) 蜂窝网络将原生基于云，并构建在可编程、虚拟化和分解架构之上。控制功能与硬件结构的分离以及标准化控制接口的引入将能够定义定制闭环控制，最终实现嵌入式智能和实时分析，从而有效实现自主和自我优化的愿景网络。本文探讨了 O-RAN 联盟提出的分解网络架构，作为 NextG 网络的关键推动者。在此架构背景下，我们讨论了数据驱动优化方法在不同时间尺度上进行网络控制的潜力、挑战和局限性。我们还首次将符合 O-RAN 标准的软件组件与开源全栈软件化蜂窝网络进行大规模集成。在全球最大的无线网络模拟器 Colosseum 上进行的实验展示了通过深度强化学习代理实现实时分析和控制的闭环集成。我们还展示了通过在近实时 RAN 智能控制器上运行的 xApp 进行无线接入网络 (RAN) 控制的可行性，以优化共存网络切片的调度策略，利用 O-RAN 开放接口收集边缘数据。网络。