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.

翻译：由于其无处不在且非接触式的特性，利用WiFi基础设施执行感知任务具有巨大潜力。然而，WiFi接收机测量的信道状态信息（CSI）在增益和相位上均存在误差，这可能会严重阻碍感知任务。通过分析不同WiFi接收机的这些误差，本研究建立了增益和相位误差的数学模型。基于这些模型，提出了若干具有理论依据的预处理算法，用于在接收机端纠正此类误差，从而获得干净的CSI。仿真结果表明，在典型系统参数下，与用于增益校正和相位校正的基线方法相比，所开发的CSI清理算法可分别将噪声降低40%和200%，且计算成本未显著增加。所提方法的优越性也在一个真实世界的心率监测测试平台（一个示例感知任务）中得到验证，在该任务中，与基线方法相比，估计信噪比提升了20%。