## 2 Related Works
### Wireless Positioning Systems  
Wireless positioning technology can be categorized based on the scope of service, including positioning systems for Wireless Wide Area Network (WWAN) and Wireless Local Area Network (WLAN)/Personal Area Network (PAN). The WLAN/PAN includes WiFi positioning systems, Bluetooth positioning systems, and UWB positioning systems, while the WWAN includes GNSS and cellular network positioning systems. WiFi-based positioning technologies mainly consist of four types, including positioning methods based on RSS, fingerprinting, AOA, and TOA. Different from traditional RSS-based WiFi positioning systems, the use of deep learning methods has been widely applied to explore the numerical features of signals in RSSI positioning. [PERSON] et al. [22] used a multi-layer neural network (MLNN) to provide localization services in RSS-based indoor localization, which combined the RSS signal-transforming section, raw data-denoising section, and node-locating section to form a deep architecture. By using the deep architecture, the predicted locations of UE can be attained without using a radio pathloss model or comparing with a radio map. [PERSON] et al. [23] emphasized the superiority of RNN in dealing with location nonlinearly because the mapping from RSS to UE's location is non-linear. In this work, the authors provided a complete study of several RNN architectures for WiFi RSS fingerprint positioning. Research on WiFi fingerprint positioning typically utilizes the CSI or RSS signals obtained from WiFi signals [24]. The CSI-based method provides more detailed signal propagation characteristics, resulting in better positioning accuracy compared to RSS [25]. However, the acquisition of CSI requires the cooperationof WiFi access points (APs), which is limited by the practical deployment of devices. To overcome this obstacle, [PERSON] et al. [26] proposed a CSI fingerprinting-based positioning approach named CRISLoc, which obtains the packets in the air passively, while a joint clustering and outlier detection method is used to find altered APs. By applying CRISLoc, the accuracy of CSI fingerprinting-based localization can reach a sub-meter level. The RSS-based WiFi fingerprint positioning technology is often heavily influenced by noisy environments; recent studies have begun to utilize advanced deep-learning models to address these issues. [PERSON] et al. [27] proposed an LF-DLSTM framework to alleviate the noise effect and attain stable features from the raw noisy RSS data. Additionally, in traditional AOA WiFi positioning systems, a limited number of antennas in WiFi devices can result in limited AOA resolution. In order to achieve more accurate and robust positioning, recent research has focused on developing new methods. [PERSON] et al. [28] worked out the relationships among different AoAs of different APs, and proposed a novel co-localization method between multiple APs to achieve a real-time and accurate localization system. For TOF-based WiFi positioning, the positioning performance of the TOF-based WiFi system is largely limited by the WiFi channel bandwidth because of the low resolution of TOF, and recent research has utilized multipath to increase time resolution [29]. While the use of WIFI technology for positioning facilities can achieve centimeter-level accuracy in many studies, the coverage range is limited to a 10 m level, which results in extremely high deployment costs when attempting to cover large areas. The Bluetooth positioning system is a common short-distance wireless communication technology primarily used for PAN. In the latest release of Bluetooth 5.1, the version has added measurements of AOA and AOD, integrating the results with RSS to provide sub-meter positioning accuracy [30]. However, Bluetooth positioning faces serious multipath interference issues. The accuracy of positioning is difficult to further improve, and there are limitations on coverage range, making it challenging to deploy over large areas [31].