VAWSS: Variational Autoencoder-Enhanced Wireless Sensing Simulator for WiFi Channel State Information

Wireless sensing technology represents a significant milestone in the evolution of modern wireless communication toward sensing capabilities. Current commercial WiFi devices are known to perform various non-contact detections, such as respiration monitoring and gesture recognition, by capturing Channel State Information (CSI). However, the inherent unpredictability of signal fluctuations in real-world environments is a challenge for its application. For example, CSI is highly susceptible to environmental interference in real-world settings, which complicates its usage for the design and analysis of sensing systems. This further leads to limited availability of compatible commodity devices, making the collection of CSI datasets difficult and expensive. Therefore, the deployment of CSI devices and the method of reducing environmental interference present significant challenges in the field of wireless sensing. To effectively mitigate these issues, we employ GPU-based ray-tracing technology combined with a Variational Auto Encoder (VAE) to simulate CSI variations for analysis. Field experiments have been conducted to evaluate the performance of our simulator.