基于生成式AI的空间信道状态信息预测：迈向全息通信与数字无线电孪生
Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin
AI总结：
本文面向6G大规模天线管理，提出融合全息通信与数字孪生的数字无线电孪生框架，并探索用生成式AI预测空间信道状态信息中的有效路径，验证其在推动6G演进中的潜力。
摘要：
随着5G技术日益成熟，人们对6G的期待不断增长，6G有望通过前沿无线电技术提供更快、更可靠的无线连接。然而，如何高效管理这些无线电技术所部署的大规模天线阵列至关重要。传统的管理方法主要是被动式的，通常基于用户反馈来适应动态无线信道。然而，一种更具前景的方法在于空间信道状态信息（spatial-CSI）的预测，这是一种全包含的信道表征，包含发射机（Tx）与接收机（Rx）之间所有可行的视距（LoS）和非视距（NLoS）路径，以及每条路径的三维（3D）轨迹、衰减、相移、时延和极化。硬件和神经网络的进步使得利用精确环境信息预测此类spatial-CSI成为可能，并进一步探索全息通信的可能性，这意味着对发射无线电波的每一个方面实现完全控制。基于全息通信与数字孪生的融合，我们提出了一个新的框架——数字无线电孪生，该框架同时利用数字世界的优势和无线电波的确定性控制，支持广泛的高层应用。作为朝这一愿景方向的初步尝试，本文探索了使用生成式人工智能（AI）来精确定位给定环境中的有效路径，展示了有前景的结果，并凸显了该方法在推动6G无线通信技术演进方面的潜力。
英文摘要：
As 5G technology becomes increasingly established, the anticipation for 6G is growing, which promises to deliver faster and more reliable wireless connections via cutting-edge radio technologies. However, efficient management method of the large-scale antenna arrays deployed by those radio technologies is crucial. Traditional management methods are mainly reactive, usually based on feedback from users to adapt to the dynamic wireless channel. However, a more promising approach lies in the prediction of spatial channel state information (spatial-CSI), which is an all-inclusive channel characterization and consists of all the feasible line-of-sight (LoS) and non-line-of-sight (NLoS) paths between the transmitter (Tx) and receiver (Rx), with the three-dimension (3D) trajectory, attenuation, phase shift, delay, and polarization of each path. Advances in hardware and neural networks make it possible to predict such spatial-CSI using precise environmental information, and further look into the possibility of holographic communication, which implies complete control over every aspect of the radio waves emitted. Based on the integration of holographic communication and digital twin, we proposed a new framework, digital radio twin, which takes advantages from both the digital world and deterministic control over radio waves, supporting a wide range of high-level applications. As a preliminary attempt towards this visionary direction, in this paper, we explore the use of generative artificial intelligence (AI) to pinpoint the valid paths in a given environment, demonstrating promising results, and highlighting the potential of this approach in driving forward the evolution of 6G wireless communication technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
