The 6G wireless system is expected to deliver faster and more reliable wireless connections via cutting-edge radio technologies. However, 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. In this paper, the authors explored generative artificial intelligence to accurately model the environment particularly for radio simulations and identify valid paths within it for real-time spatial-channel state information (CSI) prediction. Different from the traditional management methods that predominantly rely on user feedback to adapt to dynamic wireless channels, the proposed approach lies in the prediction of 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 and receiver, with three-di-mensional (3D) trajectory, attenuation, phase shift, delay, and polarization of each path. The authors argued that by exploiting the multi-path effect, it can enhance the spatial capabilities of wire-less channels using antenna arrays and multiple-input multiple-output (MIMO) configurations for 6G wireless communication technologies.

中文翻译：

---

 扫描文献


6G 无线系统预计将通过尖端无线电技术提供更快、更可靠的无线连接。然而，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。在本文中，作者探索了生成人工智能来准确地对环境进行建模，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信道状态信息（CSI）预测。与主要依靠用户反馈来适应动态无线信道的传统管理方法不同，所提出的方法在于预测空间CSI，这是一种信道特征，由所有鲁棒视距（LoS）和非视距组成。 -发射器和接收器之间的视距 (NLoS) 路径，以及每条路径的三维 (3D) 轨迹、衰减、相移、延迟和偏振。作者认为，通过利用多径效应，可以使用 6G 无线通信技术的天线阵列和多输入多输出 (MIMO) 配置来增强无线信道的空间能力。