The evolution of the Radio Access Network (RAN) in 5G and 6G technologies marks a shift toward open, programmable, and softwarized architectures, driven by the Open RAN paradigm. This approach emphasizes open interfaces for telemetry sharing, intelligent data-driven control loops for network optimization, and virtualization and disaggregation of multi-vendor RAN components. While promising, this transition introduces significant challenges, including the need to design interoperable solutions, acquire datasets to train and test AI/ML algorithms for inference and control, and develop testbeds to benchmark these solutions. Experimental wireless platforms and private 5G deployments play a key role, providing architectures comparable to real-world systems and enabling prototyping and testing in realistic environments. This dissertation focuses on the development and evaluation of complementary experimental platforms: Colosseum, the world's largest Open RAN digital twin, and X5G, an open, programmable, multi-vendor private 5G O-RAN testbed with GPU acceleration. The main contributions include: (i) CaST, enabling automated creation and validation of digital twin wireless scenarios through 3D modeling, ray-tracing, and channel sounding; (ii) validation of Colosseum digital twins at scale, demonstrating that emulated environments closely reproduce real-world setups; (iii) X5G, integrating NVIDIA Aerial GPU-accelerated PHY processing with OpenAirInterface higher layers; (iv) a GPU-accelerated dApp framework for real-time RAN inference, enabling sub-millisecond control loops for AI-native applications including ISAC; and (v) intelligent RAN applications spanning spectrum sharing, interference detection, network slicing, security, and CSI-based sensing. Overall, this dissertation provides an end-to-end methodology bridging digital and physical experimentation for next-generation cellular networks.

翻译：5G与6G技术中无线接入网（RAN）的演进，在开放无线接入网（Open RAN）范式的推动下，正朝着开放、可编程和软件化的架构转变。这一方法强调：通过开放接口实现遥测数据共享，利用智能数据驱动控制环路进行网络优化，以及对多厂商RAN组件进行虚拟化与解耦。尽管前景广阔，这一转型也带来了重大挑战，包括需要设计互操作性解决方案、获取用于训练和测试AI/ML推理与控制算法的数据集，以及开发测试平台来对这些解决方案进行基准测试。实验性无线平台和私有5G部署在其中扮演着关键角色，它们提供了与现实系统相当的架构，并支持在真实环境中进行原型设计和测试。本论文聚焦于开发和评估两个互补的实验平台：Colosseum——全球最大的Open RAN数字孪生平台，以及X5G——一个具备GPU加速功能的开放、可编程、多厂商私有5G O-RAN测试平台。主要贡献包括：（i）CaST，通过3D建模、射线追踪和信道探测，实现数字孪生无线场景的自动化创建与验证；（ii）大规模验证Colosseum数字孪生，证明其仿真环境能够高度复现真实世界设置；（iii）X5G，将NVIDIA Aerial GPU加速的物理层处理与OpenAirInterface高层协议栈集成；（iv）一个用于实时RAN推理的GPU加速dApp框架，为包括ISAC在内的AI原生应用提供亚毫秒级控制环路；（v）涵盖频谱共享、干扰检测、网络切片、安全以及基于CSI的感知等领域的智能RAN应用。总体而言，本论文提供了一种端到端的方法论，为下一代蜂窝网络架起了数字与物理实验之间的桥梁。