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Aerial CUDA-Accelerated RAN - Home

Aerial CUDA-Accelerated RAN

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Aerial CUDA-Accelerated RAN - Home

Aerial CUDA-Accelerated RAN

  • PDF

Table of Contents

  • Aerial cuBB
    • Getting Started
    • Product Brief
      • cuPHY Features Overview
      • Aerial CUDA-Accelerated RAN Features for 5G gNB
      • Supported Systems
      • Operations, Administration, and Management (OAM) Guide
        • Operation
        • Fault Management
        • Configuration
    • cuBB Release Notes
      • Software Manifest
      • Supported Features and Configurations
      • Multicell Capacity
      • Supported Test Vector Configurations
      • Limitations
      • Acknowledgements
    • cuBB Installation Guide
      • Installing Tools on Grace Hopper MGX System
      • Installing Tools on Dell R750
      • Installing and Upgrading Aerial cuBB
      • cuBB on NVIDIA Cloud Native Stack
      • Aerial System Scripts
      • CUBB Aerial SDK Versioning in YAML Files
      • Troubleshooting
    • cuBB Quickstart Guide
      • cuBB Quickstart Overview
      • Generating TV and Launch Pattern Files
      • Running Aerial cuPHY
      • Running cuBB End-to-End
      • Running cuBB End-to-End Perf tests
      • E2E gNodeB on MIG
      • Active-Standby Fronthaul Port Failover
    • cuBB Integration Guide
      • NVIPC
        • NVIPC Overview
        • NVIPC Integration
      • SCF FAPI Support
    • cuBB Developer Guide
      • Overview
      • Components
      • Test MAC and RU Emulator Architecture Overview
      • 5G MATLAB Models for Testing and Validation
      • AI/ML Components for PUSCH Channel Estimation
      • References
    • Glossary
  • Aerial cuMAC
    • Getting Started with cuMAC
    • cuMAC API Reference
    • Examples
    • cuMAC-CP integration guide
      • cuMAC-CP API Procedures
      • cuMAC-CP API Messages
      • L2 integration notes
      • cuMAC-CP Tests
  • Aerial Data Lake
  • 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 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
    • API Reference
      • Physical layer for 5G
        • Receiver algorithms
        • Configuration classes
        • PDSCH
        • PUSCH
        • LDPC 5G
        • Sounding reference signals (SRS)
        • Fading channel
        • API definitions
      • Utilities
  • pyAerial

pyAerial#

PyAerial provides a Python API towards the 5G signal processing functionality included in the Aerial cuPHY library.

  • Overview
    • Key Features
    • Target Audience
    • Value Proposition
    • Release Notes
  • Getting Started with pyAerial
    • Pre-requisites
    • Installing pyAerial
    • Testing the installation
    • Running the example Jupyter notebooks
  • Examples of Using pyAerial
    • Running a PUSCH link simulation
    • LDPC encoding-decoding chain
    • Sounding reference signal transmission and reception
    • Dataset generation by simulation
    • Dataset generation for LLRNet
    • LLRNet model training
    • Neural receiver validation
    • Machine learning based channel estimation for 5G NR PUSCH
    • Channel estimation on transmissions captured using Aerial Data Lake
    • Decoding PUSCH transmissions captured using Aerial Data Lake
  • API Reference
    • Physical layer for 5G
    • Utilities

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Last updated on Apr 18, 2025.