Optimal Preprocessing of WiFi CSI for Sensing Applications

Due its ubiquitous and contact-free nature, the use of WiFi infrastructure for performing sensing tasks has tremendous potential. However, the channel state information (CSI) measured by WiFi devices suffers from errors in both its gain and phase, which can significantly hinder sensing tasks. By analyzing these errors from different WiFi devices, a mathematical model for these gain and phase errors is developed in this work. Based on these models, several theoretically justified preprocessing algorithms for correcting such errors and, thus, obtaining clean CSI are presented. Simulation results show that at typical system parameters the developed algorithms for cleaning CSI can reduce noise by 40% and 200%, respectively, compared to baseline methods for gain correction and phase correction, without significantly impacting computational cost. The superiority of proposed methods is also validated in a real world testbed for respiration rate monitoring (an exemplary sensing task), where they improve estimation signal-to-noise ratio by 10% compared to baseline methods.