NVIDIA Aerial

NVIDIA Aerial™ is a suite of accelerated computing platforms, software, and services for designing, simulating, and operating wireless networks. Aerial contains hardened RAN software libraries for telcos, cloud service providers (CSPs), and enterprises building commercial 5G networks. Academic and industry researchers can access Aerial on cloud or on-premises setups for advanced wireless and AI/machine learning (ML) research for 6G.arch.

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NVIDIA Aerial RAN brings together the Aerial software for 5G and AI frameworks and the NVIDIA accelerated computing platform, enabling TCO reduction and unlocking infrastructure monetization for telcos.
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Aerial Omniverse Digital Twin is a large-scale simulation platform that powers physically accurate wireless environments. Aerial Omniverse Digital Twin leverages NVIDIA GPUs to deliver the highest level of performance needed to enable a realistic NDT. This accelerates the evolution of new features in wireless networks by giving you a new paradigm shift to design, test and deploy wireless networks.

The NVIDIA Aerial AI Radio Frameworks enables training and inference in the RAN. The platform tools—pyAerial, NVIDIA Aerial™ Data Lake, and Sionna—span the research space from AI and machine learning (AI/ML) algorithm exploration, training, and inference to simulation and real-time implementation in a GPU-accelerated, over-the-air network testbed such as NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA).

Sionna™ is a GPU-accelerated open-source library for link-level simulations. It enables rapid prototyping of complex communication system architectures and provides native support for the integration of machine learning in 6G signal processing.
Aerial Data Lake can be used in conjunction with the NVIDIA pyAerial library to generate training data for layer-1 pipelines built on neural networks. An Aerial Data Lake database consists of RF samples from a 7.2x fronthaul interface together with layer-2 meta-information to enable database search and query operations. A pyAerial pipeline can access samples from Aerial Data Lake database using the Data Lake Python APIs, and transform that data into training data for any function in the pipeline. Figure 2 illustrates data ingress from a Data Lake database into a pyAerial pipeline and using standard Python file I/O to generate training data for a soft de-mapper.
pyAerial is a Python library of physical layer components that can be used as part of the workflow in taking a design from simulation to real-time operation. It helps with end-to-end verification of a neural receiver, and helps bridge the gap from the world of training and simulation in TensorFlow/PyTorch to real-time operation in an over-the-air testbed.
NGC link for Aerial CUDA-Accelerated RAN
NGC link for Aerial Omniverse Digital Twin
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04/20/24
Archive version of Aerial SDK docs
04/20/24
Archive version of Aerial SDK docs
04/20/24
Archive version of Aerial SDK docs
04/20/24
Archive version of Aerial SDK docs