Pushing Wi-Fi Towards Fine-Grained Sensing Via Spectrogram Enhancement

In recent years, Wi-Fi sensing has attracted much attention due to the widespread deployment of communication devices. Due to advancements in signal processing algorithms, contactless sensing technology based on Wi-Fi signals has now been widely applied. However, the limited bandwidth of Wi-Fi systems constrains the performance of Wi-Fi sensing, posing challenges for accomplishing more fine-grained tasks (distinguishing more gestures or multiple targets, etc.). To address this challenge, in this paper, we design a spectrogram enhancement network for Wi-Fi channel state information (CSI) based on the characteristics of Wi-Fi signals to improve the sensing capability of Wi-Fi signals. Specifically, we use a neural network to generate super-resolution spectrograms of CSI to distinguish different time-frequency components in the environment at a finer granularity. Through extensive evaluation, we demonstrate that our designed system can achieve finer-grained perception accuracy than the state-of-the-art systems.