Skip to main content
Ctrl+K
Aerial CUDA-Accelerated RAN - Home

Aerial CUDA-Accelerated RAN

  • PDF
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
      • SCF FAPI Support
      • Limitations
      • Acknowledgements
    • cuBB Installation Guide
      • Installing Tools on Grace Hopper MGX System
      • Installing Tools on Dell R750
      • Installing and Upgrading Aerial cuBB
      • Aerial System Scripts
      • Troubleshooting
    • cuBB Quickstart Guide
      • cuBB Quickstart Overview
      • Generating TV and Launch Pattern Files
      • Running Aerial cuPHY
      • Running cuBB End-to-End
      • cuBB on NVIDIA Cloud Native Stack
    • cuBB Integration Guide
      • cuMAC-CP
        • cuMAC-CP API Procedures
        • cuMAC-CP API Messages
        • L2 integration notes
        • Tests
      • NVIPC
        • NVIPC Overview
        • NVIPC Integration
    • 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
  • 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 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
        • PDSCH
        • PUSCH
        • LDPC 5G
        • Fading channel
        • Parameter classes
      • Utilities
  • pyAerial
  • API Reference

API Reference#

Contents:

  • Physical layer for 5G
    • Receiver algorithms
    • PDSCH
    • PUSCH
    • LDPC 5G
    • Fading channel
    • Parameter classes
  • Utilities
    • FAPI and Matlab interface utilities
    • Data storing utilities
    • CUDA utilities

previous

Using pyAerial for PUSCH decoding on Aerial Data Lake data

next

Physical layer for 5G
NVIDIA NVIDIA

Copyright © 2024-2025, NVIDIA Corporation.

Last updated on Feb 07, 2025.