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 24-2
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