Abstract
Various commercial Wi-Fi based applications have been proposed, such as indoor positioning, indoor tracking, behavior recognition, gesture recognition, etc. These applications need to use the channel state information (CSI) fed back by Open-Source wireless network card devices, but without prior compression, the CSI feedback overhead is huge, which is not conducive to practical applications. Therefore, in order to promote the application of commercial Wi-Fi, CSI compression plays a key role. In this paper, we compare two compression methods. One is a novel curve fitting method, which can compress CSI by fed back the fitting parameters of CSI curve. The other one is the classical compressed sensing method, which can restore the original signal from a small number of measurements by using the sparsity of the signal specific domain. Using the CSI data collected from commercial Wi-Fi devices, we test the effect of the two compression methods, and compare the effects of the two compression methods on feature extraction with the same CSI feedback overhead. The experimental results show that the compression scheme is feasible.
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Acknowledgement
This research was supported in part by the National Natural Science Foundation of China (61771083, 61704015), Science and Technology Research Project of Chongqing Education Commission (KJQN201800625) and Chongqing Natural Science Foundation Project (cstc2019jcyj-msxmX0635).
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Wang, X., Yang, X., Zhou, M., Xie, L. (2021). Performance Comparison of Curve Fitting and Compressed Sensing in Channel State Information Feature Extraction. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_53
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DOI: https://doi.org/10.1007/978-3-030-78612-0_53
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