For massive multiple-input multiple-output systems in the frequency division duplex (FDD) mode, accurate downlink channel state information (CSI) is required at the base station (BS). However, the increasing number of transmit antennas aggravates the feedback overhead of CSI. Recently, deep learning (DL) has shown considerable potential to reduce CSI feedback overhead. In this paper, we propose a Swin Transformer-based autoencoder network called SwinCFNet for the CSI feedback task. In particular, the proposed method can effectively capture the long-range dependence information of CSI. Moreover, we explore the impact of the number of Swin Transformer blocks and the dimension of feature channels on the performance of SwinCFNet. Experimental results show that SwinCFNet significantly outperforms other DL-based methods with comparable model sizes, especially for the outdoor scenario.

翻译：对于频分双工（FDD）模式下的大规模多输入多输出系统，基站（BS）需要准确的下行信道状态信息（CSI）。然而，发射天线数量的增加加剧了CSI的反馈开销。近年来，深度学习（DL）在降低CSI反馈开销方面展现出巨大潜力。本文提出一种基于Swin Transformer的自编码器网络SwinCFNet，用于CSI反馈任务。特别地，该方法能有效捕获CSI的长程依赖信息。此外，我们探究了Swin Transformer模块数量及特征通道维度对SwinCFNet性能的影响。实验结果表明，在模型规模相当的情况下，SwinCFNet的性能显著优于其他基于深度学习的方法，尤其在室外场景中表现突出。