NVIDIA AI Aerial: AI-Native Wireless Communications

6G brings a paradigm shift towards AI-native wireless systems, necessitating the seamless integration of digital signal processing (DSP) and machine learning (ML) within the software stacks of cellular networks. This transformation brings the life cycle of modern networks closer to AI systems, where models and algorithms are iteratively trained, simulated, and deployed across adjacent environments. In this work, we propose a robust framework that compiles Python-based algorithms into GPU-runnable blobs. The result is a unified approach that ensures efficiency, flexibility, and the highest possible performance on NVIDIA GPUs. As an example of the capabilities of the framework, we demonstrate the efficacy of performing the channel estimation function in the PUSCH receiver through a convolutional neural network (CNN) trained in Python. This is done in a digital twin first, and subsequently in a real-time testbed. Our proposed methodology, realized in the NVIDIA AI Aerial platform, lays the foundation for scalable integration of AI/ML models into next-generation cellular systems, and is essential for realizing the vision of natively intelligent 6G networks.

Spectral Efficiency Considerations for 6G

As wireless connectivity continues to evolve towards 6G, there is an ever-increasing demand to not only deliver higher throughput, lower latency, and improved reliability, but also do so as efficiently as possible. To this point, the term efficiency has been quantified through applications to Spectral Efficiency (SE) and Energy Efficiency (EE). In this paper we introduce a new system metric called Radio Resource Utilization Efficiency (RUE). This metric quantifies the efficiency of the available radio resources (Spectrum, Access Method, Time Slots, Data Symbols, etc.) used to deliver future 6G demands. We compare the system performance of Typical Cellular and Cell-Free Massive MIMO deployments as a vehicle to demonstrate the need for this new metric. We begin by providing a concise treatment of items impacting SE by introducing three categories: 5G Radio Resources, Practical Limitations (such as channel matrix rank deficiency) and Implementation Losses (SINR degradation). For the example Radio Access Technology configuration analyzed, we show 5G yields an RUE of 47% (revealing significant room for improvement when defining 6G). Practical limitation assumptions are compared to 5G Multi-User MIMO (MU-MIMO) measurements conducted in a commercialized deployment. SE losses are characterized to offer guidance to advanced algorithms employing Machine Learning (ML) based techniques. We present the benefits of increasing the transmission Bandwidth (BW) from 100MHz to 1.6GHz. We describe a Next Generation RAN architecture that can support 6G and AI-RAN.