Maximize Spectral Efficiency with AI-Native RAN and NVIDIA AI Aerial

Summary

Massive MIMO deployments often fail to achieve theoretical spectral efficiency because CPU compute constraints force algorithmic compromises in user tracking, interference mitigation, and pairing. NVIDIA AI Aerial addresses this gap using a GPU-accelerated, AI-native radio access network architecture. This approach enables dense mathematical models across Layer 1 and Layer 2 workloads, including machine-learning beamforming and deep reinforcement learning link adaptation. In 64T64R simulations, AI beamforming achieved up to 1.62x higher throughput at 32 layers compared to regularized Zero Forcing baselines. The platform also unifies physical and MAC layer coordination while allowing dynamic allocation of spare GPU compute to edge AI inference.

Context

Massive MIMO systems in field deployments operate below theoretical spectral efficiency due to compute limitations on CPUs, which struggle with accurate user tracking, signal interference, and efficient user pairing during simultaneous data transmission.

Approach / What changed

NVIDIA AI Aerial uses GPU acceleration and an AI-native parallel computing architecture to execute complex Layer 1 and Layer 2 algorithms, such as machine-learning beamforming weight generation and deep reinforcement learning link adaptation, while sharing data spaces between cuPHY and cuMAC.

Takeaways

• In a 64T64R MU-MIMO scenario, AI-based beamforming delivered a 1.28x throughput improvement at 16 layers and up to 1.62x at 32 layers over regularized Zero Forcing, despite increasing compute from 272M to 2.58B FLOPs per cell.
• A field trial with SoftBank and NVIDIA demonstrated stable outdoor 16-layer massive MU-MIMO on a GPU-based AI-RAN platform, achieving roughly 3x the spectral efficiency of a conventional 4-layer baseline.
• Deep reinforcement learning link adaptation combined with channel-orthogonality-based user pairing achieved a 1.3x throughput gain over outer-loop link adaptation at the cell edge while staying within the ~30 microsecond inference budget.

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