NVIDIA AI Aerial: AI-Native Wireless Communications

• URL: http://arxiv.org/abs/2510.01533v1
• Date: Thu, 02 Oct 2025 00:10:20 GMT
• Title: NVIDIA AI Aerial: AI-Native Wireless Communications
• Authors: Kobi Cohen-Arazi, Michael Roe, Zhen Hu, Rohan Chavan, Anna Ptasznik, Joanna Lin, Joao Morais, Joseph Boccuzzi, Tommaso Balercia,
• Abstract summary: 6G brings a paradigm shift towards AI-native wireless systems.<n>We propose a robust framework that compiles Python-based algorithms into GPU-run blobs.<n>We demonstrate the efficacy of performing the channel estimation function in the PUSCH receiver through a convolutional neural network (CNN) trained in Python.
• Score: 1.199345934137053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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