Design and Evaluation of Next-Generation Cellular Networks through Digital and Physical Open and Programmable Platforms

2026-01-26

AI Summary

This work addresses key challenges in the evolution of open and programmable radio access networks (O-RAN) for 5G/6G, including interoperability, acquisition of AI/ML training data, and validation in real-world environments. To this end, the authors integrate Colosseum—the world’s largest O-RAN digital twin platform—with X5G, a multi-vendor GPU-accelerated private 5G testbed, and propose CaST, an automated scenario generation methodology enabling high-fidelity digital twinning and end-to-end validation. By combining NVIDIA Aerial GPU-accelerated physical layer processing, the OpenAirInterface protocol stack, and a sub-millisecond control-loop dApp framework, the platform successfully supports the development and validation of intelligent RAN applications, including dynamic spectrum sharing, interference detection, network slicing, security mechanisms, and integrated sensing and communication.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Open RAN
• 5G/6G
• digital twin
• experimental platform
• AI/ML for RAN

Innovation

• Open RAN
• Digital Twin
• GPU-Accelerated PHY
• AI-Native RAN
• O-RAN Testbed