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Aerial CUDA-Accelerated RAN - Home Aerial CUDA-Accelerated RAN - Home

Aerial CUDA-Accelerated RAN

  • pdf
  • github
  • www
Aerial CUDA-Accelerated RAN - Home Aerial CUDA-Accelerated RAN - Home

Aerial CUDA-Accelerated RAN

  • pdf
  • github
  • www

Table of Contents

  • Overview
  • Features and Capabilities
    • cuPHY Features
    • cuMAC Features
  • Release Notes
    • Latest Release (26-2)
    • Previous Releases
    • Limitations
  • Installation Guide
    • Software Manifest
    • Installing Tools on MGX ARC Pro System
    • Installing Tools on Grace Hopper MGX System
    • Installing Tools on NVIDIA DGX Spark System
    • Installing Tools on Dell R750
    • Installing and Upgrading cuBB
    • Aerial System Scripts
    • Aerial CUDA-Accelerated RAN Versioning in YAML Files
    • Troubleshooting
  • Quickstart Guide
    • Quickstart Overview
    • Generating TV and Launch Pattern Files
    • Running Aerial cuPHY
    • Running cuBB End-to-End
    • Running cuBB Performance tests
    • E2E gNodeB on MIG
  • cuBB
    • cuBB Integration Guide
      • NVIPC
        • NVIPC Overview
        • NVIPC Integration
      • FAPI Support
        • Standard FAPI Support
        • Vendor-Specific Extensions
        • FAPI Message Formats and Specifications
      • Run-time Configuration and Status
      • 64 TR MU-MIMO Support with Static and Dynamic Beamforming
    • cuPHY Developer Guide
      • cuPHY Software Architecture
      • cuPHY Components
      • 5G MATLAB Models for Testing and Validation
      • AI/ML Components for PUSCH Channel Estimation
      • References
    • cuMAC
      • Overview
      • cuMAC Developer Guide
      • cuMAC-CP integration guide
        • cuMAC API Reference
        • cuMAC-CP API Procedures
        • cuMAC-CP API Messages
        • L2 integration notes
  • Data Lake
  • dApp Framework
  • Tools
    • TestMAC
    • RU Emulator
    • pyAerial
      • Overview
      • Getting Started with pyAerial
      • Examples of Using pyAerial
        • Using pyAerial to run a PUSCH link simulation
        • Using pyAerial for LDPC encoding-decoding chain
        • Using pyAerial to run 5G sounding reference signal transmission and reception
        • Using pyAerial to run CSI-RS transmission and reception
        • Using pyAerial for data generation by simulation
        • LLRNet: Dataset generation
        • LLRNet: Model training and testing
        • Using pyAerial to evaluate a PUSCH neural receiver
        • Channel Estimation for Uplink Shared Channel (PUSCH) in pyAerial
        • Using pyAerial for channel estimation on Aerial Data Lake data
        • Using pyAerial for PUSCH decoding on Aerial Data Lake data
        • Using pyAerial for decoding PUSCH transmissions from multiple cells using Aerial Data Lake data
        • Per-UE Uplink DMRS Channel Estimate Extraction from Aerial Data Lake
        • Per-UE SRS Metrics, Channel Estimates & IQ from Aerial Data Lake
      • API Reference
        • Physical layer for 5G
        • Utilities
    • RANPerf Testbench
  • OAM
    • Configuration
    • Operation
    • Logging
    • Metrics
  • Glossary
  • Acknowledgements
  • Tools
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Last updated on Sep 16, 2026.