Deep Learning-Based Pulse-Shaping Filter Estimation for Fine-Grained WiFi Sensing
Refereed conference paper presented and published in conference proceedings
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AbstractIn numerous WiFi sensing applications, such as passive human localization, the precision of sensing is often influenced by the estimation accuracy of multipath parameters. Several existing algorithms leverage pulse-shaping filter information to enhance multipath and channel estimation. However, WiFi chips do not disclose this filter information, and no current research has focused on measuring or estimating these pulse-shaping filters. In this paper, we introduce a new deep learning approach for the accurate estimation of pulse-shaping filters using channel state information (CSI), which incorporates both multipath channel information and pulse-shaping filter information. Specifically, we construct a convolutional neural network consisting of an encoder-regressor architecture, where the encoder translates the CSI into a latent representation, and the regressor subsequently estimates the pulse-shaping filter from this representation. Our proposed model's efficacy is demonstrated through its low normalized root mean squared error (NRMSE) in a variety of channel conditions, highlighting its ability to accurately estimate pulse-shaping filters.
All Author(s) ListHan Hu, Ruiqi Kong, Ke Xu, He Chen
Name of Conference2024 IEEE International Conference on Communications
Start Date of Conference09/06/2024
End Date of Conference13/06/2024
Place of ConferenceDenver, CO
Country/Region of ConferenceUnited States of America
Proceedings TitleICC 2024 - IEEE International Conference on Communications
Year2024
Pages4421 - 4426
LanguagesEnglish-United States
