NVIDIA open source tools advance 6G research

NVIDIA’s open source Aerial and Sionna tools are helping researchers connect network simulation and AI training with real wireless experiments as they develop 6G-ready systems. Global developer adoption of the platforms has grown 10x over the past year, and NVIDIA’s open source 5G/6G tools have surpassed 1 million downloads.

At King’s College London, researchers used compact descriptions of objects and their relationships instead of periodically transmitting full images to guide a robot arm. In fruit-packing and cube-stacking experiments with deliberately introduced faults, the approach reduced task completion time by up to 52.6% and improved task success. The system also recovered from unsuccessful actions and, with a digital twin, checked planned movements for potential collisions.

SoftBank integrated a neural receiver into NVIDIA Aerial CUDA-Accelerated RAN and tested it with a commercial 5G smartphone. Under controlled interference, it delivered higher uplink throughput across all 15 tested settings, with a maximum gain of roughly 60%. Separately, ETH Zurich researchers found that adapting neural receivers using local measurements reduced block error rates compared with selected pretrained versions.

University of Leeds researchers forecast network demand 10 minutes ahead to guide base station sleep decisions, reporting a 45.6% reduction in RAN power consumption while maintaining connectivity. CTTC is also integrating GPU-accelerated radio channel models into its open source network simulator, shortening simulation runtime while preserving expected statistical behavior and network performance.