## 2 Related Works
### CSI Data Preparation Methods  
A variety of CSI data preparation strategies have been developed to enhance the precision of Wi-Fi sensing systems that utilize deep learning, which includes methods to physically increase the number of subcarriers in the CSI measurements, strategies to filter out random noise from the measured CSI, approaches to convert CSI measurement data series into images, and techniques to reduce data dimensionality. Generally, as the dimension of the input layer in a deep learning model increases, the input data contain a more diverse set of features. Accordingly, as the input data dimension increases, the deep learning model becomes able to approximate more complex functions more correctly. To improve the accuracy of the deep learning models for Wi-Fi sensing, it has been proposed to expand the dimension of CSI data by increasing the number of subcarriers (i.e., \(K\)) and the number of transmitting and receiving antennas (i.e., \(N_{t}\), and \(N_{t}\)). As \(K\) increases, more detailed information about the Wi-Fi signal propagation environment can be obtained. According to the Wi-Fi standard, both \(K\) and the data transmission rate increase with the bandwidth of a Wi-Fi channel. For example, in the IEEE 802.11/ac standard, when the Wi-Fi bandwidth is 20 MHz, the total number of subcarriers is 56, whereas when the bandwidth increases to 40 MHz, the number of subcarriers increases to 114, resulting in an approximately 2.3 times faster data transmission speed compared to the 20 MHz case [24]. However, since the channel bandwidth of a Wi-Fi system is determined by Wi-Fi standards [25], it is not possible to arbitrarily increase the number of subcarriers in CSI data. To address this issue, the authors in [12] proposed a method of splicing multiple Wi-Fi channels to increase the number of subcarriers in CSI data for Wi-Fi sensing. As can be seen in Equation (1), CSI data are measured for each pair of transmitting and receiving antennas. Therefore, the dimension of CSI data increases with the number of transmitting and receiving antenna pairs, even when the Wi-Fi channel bandwidth is fixed [26; 27]. To enhance data transmission speeds in modern laptops and smartphones, most COTS Wi-Fi chips are equipped with multiple transmit and receive antennas. However, IoT systems like Wi-Fi sensing systems are made up of numerous microcontrollers, each typically equipped with a Wi-Fi chip having a single Tx and Rx antenna. Although the price difference between Wi-Fi chips with multiple antennas and those with a single antenna is not significant, deploying a Wi-Fi sensing system over a large area, such as a building, requires a vast number of microcontrollers. As a result, using Wi-Fi chips with multiple antennas can significantly increase the overall system installation cost. Strategies for removing random noise from CSI measurements consider CSI data arranged by measurement time as time-series data. These methods can be classified into two categories. The methods in the first category calculate statistical measures such as the median and variance of CSI data within a fixed time window to remove noise contained in the CSI measurements in the time domain [13; 15]. The methods in the second category operate in the frequency domain. They remove noise in the CSI data by transforming the CSI time series into the frequency domain and then eliminating the high-frequency components [28; 29]. These noise removal methods can easily remove impulse noise or high-frequency noise. However, since the noise present in the CSI data is not known in advance, the detailed CSI information useful for classification may be lost during the noise removal process. Spectograms are often used to convert CSI time-series data into images which contain not only the information in the time domain but also the information in the frequency domain [30; 31]. By using spectrograms, features of CSI data can be extracted simultaneously in both the time and frequency domains, which can help to improve the performance of Wi-Fi sensing.