As the deployment of 5G technology matures, the anticipation for 6G is growing, which promises to deliver faster and more reliable wireless connections via cutting-edge radio technologies. A pivot to these radio technologies is the effective management of large-scale antenna arrays, which aims to construct valid spatial streams to maximize system throughput. Traditional management methods predominantly rely on user feedback to adapt to dynamic wireless channels. However, a more promising approach lies in the prediction of spatial channel state information (spatial-CSI), which is a channel characterization that consists of all robust line-of-sight (LoS) and non-line-of-sight (NLoS) paths between the transmitter (Tx) and receiver (Rx), with three-dimensional (3D) trajectory, attenuation, phase shift, delay, and polarization of each path. Recent advances in hardware and neural networks make it possible to predict such spatial-CSI using precise environmental information, and further explores the possibility of holographic communication, which implies complete control over every aspect of the radio waves. This paper presents a preliminary exploration of using generative artificial intelligence (AI) to accurately model the environment particularly for radio simulations and identify valid paths within it for real-time spatial-CSI prediction. Our validation project demonstrates promising results, highlighting the potential of this approach in driving forward the evolution of 6G wireless communication technologies.

中文翻译：

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利用生成式人工智能进行空间信道状态信息预测：迈向全息通信和数字无线电孪生


随着 5G 技术的部署日趋成熟，人们对 6G 的期望越来越高，它有望通过尖端无线电技术提供更快、更可靠的无线连接。这些无线电技术的关键是大规模天线阵列的有效管理，其目的是构建有效的空间流以最大化系统吞吐量。传统的管理方法主要依靠用户反馈来适应动态无线信道。然而，一种更有前景的方法在于空间信道状态信息（spatial-CSI）的预测，这是一种由所有鲁棒视距（LoS）和非视距（NLoS）组成的信道表征发射器 (Tx) 和接收器 (Rx) 之间的路径，以及每条路径的三维 (3D) 轨迹、衰减、相移、延迟和极化。硬件和神经网络的最新进展使得使用精确的环境信息来预测此类空间CSI成为可能，并进一步探索了全息通信的可能性，这意味着对无线电波的各个方面的完全控制。本文提出了使用生成人工智能 (AI) 来准确建模环境（特别是无线电仿真）并识别其中的有效路径以进行实时空间 CSI 预测的初步探索。我们的验证项目展示了有希望的结果，凸显了这种方法在推动 6G 无线通信技术发展方面的潜力。