R&S, Nvidia team on AI-driven 6G wireless research
Rohde & Schwarz is working with Nvidia on a digital twin with high-fidelity ray tracing for more realistic testing of AI-enabled receivers for 5G-Advanced and 6G.
A proof of concept system to be shown at Mobile World Congress next month with a framework for testing 5G-Advanced and 6G neural receivers under realistic propagation conditions using digital twin technology.
The aim is to bridge the gap between AI-driven wireless simulations and real-world deployment for more efficient and accurate testing of next-generation receiver architectures.
“The collaboration with Nvidia marks a significant milestone in advancing AI/ML applications in wireless communications,” said Gerald Tietscher, Vice President Signal Generators, Power Supplies and Meters at Rohde & Schwarz. “Our work has already demonstrated the potential of AI in wireless system design, from custom constellations to neural receivers handling real-world impairments. Now, with the integration of digital twins and ray tracing, we are further expanding the possibilities of AI-driven signal processing.”
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At the core of the demonstration is Sionna, a GPU-accelerated open-source library for link-level simulations, which provides ray-traced wireless channel models to generate realistic RF propagation conditions. The simulation results can then be seamlessly transferred to the R&S SMW200A vector signal generator which emulates complex real-world radio channels without requiring expensive external RF fading equipment. The resulting testbed enables testing and verification of AI/ML-based receiver algorithms and supports data-driven fine-tuning of neural components using realistic training data.
To ensure that the digital twin and ray-tracing models accurately reflect real-world conditions, the simulation is calibrated with data from a dedicated channel-sounding measurement campaign set in an urban street-canyon environment. Combining these precise measurements with the Sionna library refines the ray tracer’s ability to model material interactions and electromagnetic propagation to provide a calibrated version of the digital twin of the physical RF environment.
This enables more accurate site-specific testing and validation of machine learning-based communication algorithms, including applications such as neural receivers and ML-based CSI feedback enhancements.
“Digital twin technology has transformative potential in wireless system design. By integrating advanced NVIDIA ray tracing and machine learning into receiver development, Rohde & Schwarz is paving the way for AI-native 6G networks poised to offer outstanding efficiency and innovation compared with conventional implementations,” said Soma Velayutham, Vice President of Telecommunications at Nvidia.
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