用AI原生框架让6G网络实现端到端智能通信
NVIDIA AI Aerial: AI-Native Wireless Communications
- 将Python算法编译为GPU可运行代码，打通AI与无线信号处理
- 在数字孪生和真实测试中验证CNN提升信道估计精度
- 适合6G研发者、通信架构师快速部署AI模型
6G推动无线系统向AI原生演进，要求在蜂窝网络软件栈中无缝融合数字信号处理（DSP）与机器学习（ML）。这一转变使现代网络生命周期更接近AI系统，支持模型与算法在相邻环境中持续训练、仿真与部署。本文提出一个稳健框架，可将基于Python的算法编译为GPU可执行的二进制块，实现高效、灵活且高性能的NVIDIA GPU运行。以物理上行共享信道（PUSCH）接收机中的信道估计为例，我们使用在Python中训练的卷积神经网络（CNN）完成该任务，先在数字孪生环境中验证，再在实时测试平台中部署。该方法在NVIDIA AI Aerial平台上实现，为下一代蜂窝系统中规模化集成AI/ML模型奠定基础，是实现原生智能6G网络的关键一步。
原文摘要 · Abstract (English)
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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