WiSR: Sparse Recovery for Wi-Fi Signal via Generative Adversarial Network

Recently, Wi-Fi based sensing technology has been widely studied to provide more convenient services for humans. Although previous arts claim to achieve diverse fine-grained sensing using Wi-Fi signals, most of them assume that the data such as Channel State Information (CSI) used to achieve the sensing tasks can be sufficiently collected. However, in practical, due to the competitive nature of Wi-Fi and the frequent intermittent traffic, Wi-Fi based sensing applications often encounter problems of irregular intervals and insufficient sampling. Therefore, in this paper, we propose a Wi-Fi signal sparse recovery system (WiSR) that aims to recover sufficient and uniform sensing data from unevenly spaced and under-sampled CSI. Inspired by the success of image and audio restoration, we improve the Generative Adversarial Network (GAN) to recover Wi-Fi CSI. However, the direct application of GAN technologies for image and audio to CSI is not effective due to the difference in data representation. First, to avoid spectral impairments after conversion from time domain to frequency domain, we directly operate on the original time series CSI waveforms, thus being able to recover continuous channel variations from intermittent sparse samples. Second, to enhance the above recovery process, we utilize two novel denoising methods to obtain clean CSI, and introduce restrictions in the time and frequency domains to optimize low-level features and high-frequency information, respectively. Real-world experiments show that WiSR can accurately recover CSI, even at a rate of 10 packets per second. Through practical applications of gait recognition and gesture recognition, WiSR significantly improves accuracy compared to traditional linear interpolation and cubic interpolation.