AI-Native RAN for Spectral Efficiency

This feature thread tracks the evolution of AI-Native Radio Access Networks (RAN) to maximize spectral efficiency. Initial efforts focused on overcoming compute constraints in traditional RANs by leveraging GPU acceleration for mathematically dense Layer 1 and Layer 2 models. This enables significant improvements in massive MIMO scenarios through AI-based beamforming and Deep Reinforcement Learning (DRL) for link adaptation. Field trials demonstrate substantial throughput gains and the potential for monetization of underutilized GPU resources. This post details how NVIDIA AI Aerial leverages GPU-accelerated, AI-native architectures to overcome prior compute constraints, enabling mathematically dense Layer 1 and Layer 2 models that significantly increase spectral efficiency, particularly for massive MIMO scenarios. Field trials and engineering results demonstrate that NVIDIA AI Aerial achieves up to 1.62x throughput gains in AI-based beamforming and 1.3x throughput improvements in DRL-based link adaptation compared to traditional CPU-based methods, with practical support for large models and low-latency inference critical for real-world deployment. The platform supports scalable, software-defined RAN evolution enabling dynamic model growth, cross-cell coordination, integrated sensing and communication, and monetization of underutilized GPU resources, facilitating AI-native 5G-Advanced and 6G networks in partnership with industry leaders such as Nokia.