Optimal preprocessing of WiFi CSI for sensing applications

TL;DR

This paper develops mathematical models and preprocessing algorithms to correct gain and phase errors in WiFi CSI, significantly improving sensing accuracy in tasks like respiration monitoring.

Contribution

It introduces theoretically justified preprocessing algorithms for WiFi CSI error correction, validated through simulations and real-world experiments.

Findings

CSI noise reduced by 40% and 200% with new algorithms

Improved respiration rate estimation SNR by 20%

Algorithms maintain low computational cost

Abstract

Due to 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 a WiFi receiver suffers from errors in both its gain and phase, which can significantly hinder sensing tasks. By analyzing these errors from different WiFi receivers, 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 at a receiver 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 the proposed methods is also validated in a real-world test bed for respiration rate monitoring (an example sensing task), where they improve the estimation signal-to-noise ratio by $20$% compared to baseline methods.