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SwinFi: a CSI Compression Method based on Swin Transformer for Wi-Fi Sensing
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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.
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Cited by 1 Pith paper
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A Tutorial-cum-Survey on Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook
A tutorial-cum-survey that evaluates SimCLR, SimSiam, VICReg, and Barlow Twins on three Wi-Fi CSI datasets, reporting near-supervised accuracy on two datasets but poor accuracy on a third.
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