Deep learning based channel state information (CSI) feedback in frequency division duplex systems has drawn widespread attention in both academia and industry. In this paper, we focus on integrating the Type-II codebook in the wireless communication standards with deep learning to enhance the performance of CSI feedback. In contrast to the existing deep learning based studies on the Release 16 Type-II codebook, the Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink CSI, where the performance of deep learning based conventional methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Furthermore, a weighted shortcut module is designed to facilitate the accurate reconstruction, and a two-stage loss function that combines the mean squared error and sum rate is proposed for adapting to practical multi-user scenarios. Simulation results demonstrate that our proposed deep learning based port selection and CSI reconstruction methods can improve the sum rate performance compared with the traditional R17 Type-II codebook and deep learning benchmarks.

本文旨在将深度学习应用于R17 Type-II码本的无线通信标准，以提高CSI反馈性能，从两个角度提出了采用深度学习技术来改进Type-II码本：一是利用深度学习精确选择主要角度延迟域端口，二是根据R17 Type-II码本的反馈重构下行CSI，实验表明，与传统R17 Type-II码本和深度学习基准相比，本文提出的基于深度学习的端口选择和CSI重构方法能够提高数据传输速率。

基于深度学习的二类编码本：增强CSI反馈的新思路