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 exemplary sensing task), where they improve the estimation signal-to-noise ratio by $20$% compared to baseline methods.

翻译：由于其普适性和非接触式特性，利用WiFi基础设施执行感知任务具有巨大潜力。然而，WiFi接收器测量的信道状态信息（CSI）在增益和相位上均存在误差，这会显著阻碍感知任务的执行。通过分析不同WiFi接收器中的这些误差，本文建立了增益和相位误差的数学模型。基于这些模型，提出了几种具有理论依据的预处理算法，用于在接收端校正此类误差，从而获得干净的CSI。仿真结果表明，在典型系统参数下，所提出的CSI清洗算法在增益校正和相位校正方面，相比基线方法分别可以降低噪声40%和200%，且不会显著增加计算成本。所提方法的优越性还在一个真实测试台（用于呼吸速率监测，即一个示例感知任务）中得到验证，相比基线方法，其估计信噪比提升了20%。