In multiple-input multiple-output (MIMO) systems, the high-resolution channel
information (CSI) is required at the base station (BS) to ensure optimal
performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In
the absence of channel reciprocity in frequency division duplex (FDD) systems,
the user needs to send the CSI to the BS. Often the large overhead associated
with this CSI feedback in FDD systems becomes the bottleneck in improving the
system performance. In this paper, we propose an AI-based CSI feedback based on
an auto-encoder architecture that encodes the CSI at UE into a low-dimensional
latent space and decodes it back at the BS by effectively reducing the feedback
overhead while minimizing the loss during recovery. Our simulation results show
that the AI-based proposed architecture outperforms the state-of-the-art
high-resolution linear combination codebook using the DFT basis adopted in the
5G New Radio (NR) system.

本文提出了一种基于自编码器架构的基于 AI 的 CSI 反馈方法，通过将 CSI 编码成低维度潜空间并在 BS 处解码回来来有效地减少反馈开销，同时最小化恢复期间的损失，仿真结果表明，该 AI-based 建议的架构优于采用 5G New Radio（NR）系统中采用的 DFT 基础上的最先进的高分辨率线性组合码书。