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For massive multiple-input multiple-output systems in the frequency divisionduplex (FDD) mode, accurate downlink channel state information (CSI) isrequired at the base station (BS). However, the increasing number of transmitantennas aggravates the feedback overhead of CSI. Recently, deep learning (DL)has shown considerable potential to reduce CSI feedback overhead. In thispaper, we propose a Swin Transformer-based autoencoder network called SwinCFNetfor the CSI feedback task. In particular, the proposed method can effectivelycapture the long-range dependence information of CSI. Moreover, we explore theimpact of the number of Swin Transformer blocks and the dimension of featurechannels on the performance of SwinCFNet. Experimental results show thatSwinCFNet significantly outperforms other DL-based methods with comparablemodel sizes, especially for the outdoor scenario.

Swin Transformer-Based CSI Feedback for Massive MIMO