Aerial CUDA-Accelerated RAN

Aerial CUDA-Accelerated 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.

Aerial CUDA-Accelerated RAN has the following key features:

• Software-defined, scalable, modular, highly programmable and cloud-native, without any fixed function accelerators. Enables the ecosystem to flexibly adopt necessary modules for their commercial products.

• Full-stack acceleration of DU L1, DU L2+, CU, UPF and other network functions, enabling workload consolidation for maximum performance and spectral efficiency, leading to best-in-class system TCO.

• General purpose infrastructure, with multi-tenancy that can power both traditional workloads and cutting-edge AI applications for best-in-class RoA.

What’s New in 24-3

The following new features are available in release 24-3 for Aerial CUDA-Accelerated RAN:

• Aerial cuPHY: CUDA accelerated inline PHY

  • Multi-cell support for mMImO (up to 3 cells)

  • Scheduling DL in special slots

  • Increase SRS slots in 4T4R and mMIMO

  • SRS CS multiplexing for different UEs

  • UL PUSCH channel estimation at PRG level

  • RKHS channel estimation

• Aerial E2E: System level / End-to-End validation

  • Fronthaul Port Failover Validation (Active-Standby) of C/U/S-Planes

  • Concluded Ch.8 Conformance testing with PRACH

  • MIG validation of AI + RAN

• Aerial Redundancy/Resiliency: CUDA accelerated RAN Redundancy/Resiliency features

  • RU Health Monitor - actively detect FH connectivity issues with ORU and take corrective action

  • Introduce L1 recovery period - If L1 is running late, drop FAPI messages for some time to allow L1 to recover

  • nvIPC pcap acquisition improvements - Introduced capability to add filters (cell-id , msg-id level) to nvIPC pcap acquisition

  • Backtrace output on console - Aerial prints backtrace on console in case of crash

• Aerial cuMAC: CUDA accelerated MAC scheduler

  • DRL MCS selection module

    • Pre-trained neural networks available under aerial_sdk/cuMAC/testVectors

    • Inference based on TensorRT

  • 64TR MU-MIMO scheduler

    • UE sorting algorithm based on SRS SNR estimates

    • UE grouping algorithm based on SRS channel coefficient estimates

  • Aperiodic SRS resource manager

    • Combined with MU-MIMO UE sorting algorithm

  • 4T4R system simulation with GPU-based TDL channel model

  • Improved algorithms & CUDA implementation for type-0 and type-1 4T4R schedulers

• pyAerial: Python interface to Aerial cuPHY

  • CSI-RS transmission pipeline

  • RSRP and pre- and post-equalizer SINR estimation

  • Carrier frequency offset and timing advance estimation

  • CRC checking

  • OFDM fading channel simulation

  • Support of multiple UE groups for PUSCH receiver pipeline and its components

  • An improved API to PUSCH receiver pipeline and its components

What’s New in 24-2.1

The following new features are available in release 24-2.1 for Aerial CUDA-Accelerated RAN:

• Aerial cuPHY: CUDA accelerated inline PHY

  • 64T64R Massive MIMO:

    • 100 MHz DL max combined 16 layers + UL max combined 8 layers + SRS

    • 64T64R SRS + Dynamic + Static Beamforming Weights

    • Support multiple dynamic UE groups

    • Support flexible PRG size and PRB number

    • Support SRS buffer indexing from L2

    • Support non 2^n layers

    • Use different section IDs when splitting the C-Plane section

    • FH messaging for CSIRS + PDSCH and other channel combinations

  • Support GH200+BF3 as RU emulator platform

What’s New in 24-2

The following new features are available in release 24-2 for Aerial CUDA-Accelerated RAN:

• Aerial cuPHY: CUDA accelerated inline PHY

  • MGX Grace Hopper multicell capacity w/ telco-grade traffic model

    • 20 peak loaded 4T4R @ 100MHz

    • Capacity also validated with more challenging traffic model

      • PUSCH and PDCCH symbols in the S-slot

  • L1-L2 interface enhancements

    • Separate FAPI request timelines for PDSCH and PDCCH

• Aerial cuMAC: CUDA accelerated MAC scheduler

  • cuMAC-Sch

    • 4T4R CUDA implementation complete

  • cuMAC-CP

    • 4T4R implementation (Functional – early access)

• Aerial cuBB/E2E: System level / End-to-End validation

  • Over-The-Air (OTA) validation:

    • CBRS O-RU

    • 8 UE OTA w/ 6 UE/TTI for > 8 hours

  • RedHat-OCP:

    • Multicell capacity validated on MGX (GH200+BF3)

  • O-RAN Fronthaul:

    • 16-bit fixed point IQ sample validated E2E (Keysight eLSU)

    • Simultaneous dual-port FH capability (8 peak cells; 4 per port)

  • L2 integration:

    • Multi-L2 container instances per L1 validated E2E

• pyAerial: Python interface to Aerial cuPHY

  • TensorRT inference engine

    • Jupyter notebook example using pyAerial to validate a neural PUSCH receiver

  • LDPC API improvements

    • Added soft outputs to LDPC decoder

  • LS channel estimation

  • Limited support for Grace Hopper

    • Run pyAerial together with Aerial Data Lakes