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NVIDIA/aerial-cuda-accelerated-ran

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NVIDIA/aerial-cuda-accelerated-ran

Description: An SDK (Software Development Kit) for building commercial-grade, AI-native, 3GPP, and O-RAN compliant 5G/6G gNB software on NVIDIA-accelerated computing platforms.

Language: C++

License: NOASSERTION

Stars: 409

Forks: 138

Open issues: 13

Created: 2025-10-21T20:15:29Z

Pushed: 2026-08-27T16:16:19Z

Default branch: main

Fork: no

Archived: no

README:

NVIDIA Aerial™ CUDA-Accelerated RAN

Overview

NVIDIA Aerial™ CUDA-Accelerated RAN is a part of NVIDIA AI Aerial™, a portfolio of accelerated computing platforms, software and tools to build, train, simulate, and deploy AI-native wireless networks.

Documentation for AI Aerial™ can be found here.

The following AI Aerial™ software is available as open source:

Updates on new software releases, NVIDIA 6G events and technical training for AI Aerial™ are available via the NVIDIA 6G Developer Program.

The Aerial CUDA-Accelerated RAN SDK includes:

  • GPU-Accelerated 5G PHY (cuPHY): CUDA-based physical layer processing for 5G NR including channel coding (LDPC, Polar), modulation/demodulation, MIMO processing, and channel estimation
  • GPU-Accelerated MAC Scheduler (cuMAC): High-performance L2 scheduler acceleration for resource allocation and scheduling
  • Python API (pyAerial): Python bindings for AI/ML research and integration with frameworks like TensorFlow and Sionna
  • 5G Reference Models (5GModel): MATLAB-based 5G waveform generation and test vector creation based on 3GPP specifications
  • Containerized Environment: Docker-based development and deployment with pre-built containers

Repository Structure

aerial-cuda-accelerated-ran/
├── cuPHY/ # CUDA-accelerated Physical Layer (L1)
├── cuPHY-CP/ # Control Plane and integration components
│ ├── aerial-fh-driver/ # Fronthaul driver for O-RAN interfaces
│ ├── cuphycontroller/ # PHY controller
│ ├── cuphydriver/ # PHY driver
│ ├── cuphyl2adapter/ # L2 adapter
│ ├── ru-emulator/ # Radio Unit emulator
│ ├── testMAC/ # Test MAC implementation
│ ├── container/ # Container build scripts and recipes
│ ├── data_lake/ # data lake and E3 agent
│ └── e3agent-standalone/ # E3 Agent standalone
├── cuMAC/ # CUDA-accelerated L2 Layer
├── cuMAC-CP/ # MAC Control Plane components
├── pyaerial/ # Python API and ML/AI tools
├── 5GModel/ # TV generation for cuPHY and cuBB verification
├── testBenches/ # Test benches and performance measurement tools
├── testVectors/ # Test vectors for validation
└── cubb_scripts/ # Build and automation scripts

Getting Started

Using Pre-Built Container (Recommended)

# Clone repository
git clone --recurse-submodules https://github.com/NVIDIA/aerial-cuda-accelerated-ran.git
cd aerial-cuda-accelerated-ran

# Enable git LFS and pull files
sudo apt install git-lfs && git lfs pull

# Start interactive development container
./cuPHY-CP/container/run_aerial.sh

# Inside container: Build SDK
./testBenches/phase4_test_scripts/build_aerial_sdk.sh

Further Information

Visit the full documentation at NVIDIA Docs Hub

Contribution Guidelines

  • Aerial is not accepting contributions at this time.

Security

  • Vulnerability disclosure: [SECURITY.md](SECURITY.md)
  • Do not file public issues for security reports.

Support

  • Level: Maintained
  • How to get help:
  • File issues on GitHub for bugs and feature requests
  • Join discussions for questions and community support

License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

Note: Some dependencies may have different licenses. See [ATTRIBUTION.rst](ATTRIBUTION.rst) for third-party attributions in the source repository.

Citation

If you use NVIDIA Aerial™ CUDA-Accelerated RAN in your research, please cite:

@software{nvidia_aerial_cuda_accelerated_ran,
title = {NVIDIA Aerial™ CUDA-Accelerated RAN},
author = {NVIDIA Corporation},
year = {2025-2026},
url = {https://github.com/NVIDIA/aerial-cuda-accelerated-ran}
}