  Wi-Fi sensing is a transformative approach that enables a large of
applications through CSI analysis. The challenge lies in the high computational
and communication costs with the increasing granularity of CSI data. In this
letter, we propose SwinFi, a pioneering solution that compresses CSI at the
edge into a succinct feature image and reconstructs at the cloud for further
processing. SwinFi employs a Swin Transformer-based autoencoder-decoder
architecture that ensures SOTA performance in both CSI reconstruction and
sensing tasks. We utilize a dataset for PIR task and conduct extensive
experiments to evaluate SwinFi. The results show that SwinFi achieves the
reconstruction quality with the NMSE of -37.74dB and the classification
accuracy of 95.3% at the same time.
