# SwinFi: a CSI Compression Method based on Swin Transformer for Wi-Fi Sensing

arXiv: https://arxiv.org/abs/2405.03957 · HTML: https://mlsanity.com/paper/2405.03957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2024.

## Built on by (most cited first, 1 of 1)

- [Output Length Effect on DeepSeek-R1's Safety in Forced Thinking](https://mlsanity.com/paper/2503.01923/) (2025)

## Abstract (arXiv)

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.