# A Novel Device-Free Counting Method Based on Channel Status Information
## 2 Related Works  
CSI can evaluate the channel information on each subcarrier, and characterize the frequency selective fading characteristics of Wi-Fi channels. In addition, CSI contains the amplitude and phase information on each subcarrier, thereby enhancing richer frequency domain information. Next, we summarize the existing works of the CSI form the aspect of phase and amplitude. The amplitude-based detection. An amplitude-based scheme has attracted more attention in recent years since it is sensitive to adjacent humans, while unrelated background noise remains relatively stable. In this context, [PERSON] et al. [13] proposed a CSI feature extraction model based on the first and second largest eigenvalues of the Pearson correlation matrix of CSI amplitude, to detect the presence of humans. The existence detection is performed by outlier identification using normal features of density-based clustering. In [14], a monotonic function was proposed to depict the relationship between the crowd number and CSI amplitude variation. The function can be leveraged by a new dilated CSI matrix of the percentage of nonzero elements and the Grey Verhulst Model. In [15], a system was proposed to capture the variance between CSI amplitude of each subcarrier as features and uses Hidden Markov Model (HMM) to detect the human movement with different speed followed by a speed independent device free entity detection. In E-eyes [16], the distribution of CSI amplitude was utilized to classify the activities in entire home environment and identifies activities by calculating the similarity of each CSI segment and pre-structured activity files. [PERSON] et al. [17] designed a device-independent passive fall detection system using time stability and frequency diversity of CSI amplitude, in view of the fact the static human body is independent of the time domain variation of wireless signals. Experimental results showed that Wi-Fall can achieve a detection accuracy of 87%. The Amplitude and Phase-based detection. Despite the fact that quite a few works have studied CSI for device-free detection, most previous studies only utilized amplitude information but not phase information. However, in [18], it was found that the CSI phase information is more sensitive to the detection process. Therefore, both the amplitude and phase information are utilized to calculate the maximum eigenvalues of Pearson correlation matrices, in order to compose a two-dimensional feature to infer the existence of a moving human body. This paper further employed an SVM machine learning model to achieve human moving detection. In [19], a new feature with the coefficient of phase variation was defined. The coefficient of phase variation is the ratio of the standard deviation to the mean of the CSI phase, in which human movement is detected when the averaged ratio is set within a predefined confidence interval. [PERSON] et al. [20] proposed a device-free PAssive detection of moving humans with Dynamic Speed (PADS). The features are extracted from the covariance matrices of both CSI amplitude and phase, then, the eigenvalues of both matrices are calculated; maximum and second maximum eigenvalue of each matrix to form a four-tuple of features are selected; meanwhile, the SVM classifier to detect humans is selected. [PERSON] et al. [21] proposed an enhanced people-counting system with the DNN model as the classifier. In particular, a feature space expansion scheme is presented to enhance the DNN model. Nevertheless, this paper only showed the CSI is related to the crowd counting in indoor environment, without illustrating phase is more sensitive and explaining between the relationship CSI and the number of crowd. Although there are prior works that utilize the CSI amplitude and phase information to conduct detection of moving humans, they typically ignore the relationship between the variation of CSI amplitude or phase and the number of moving people. In our work, an explicit relationship description is concentrated.