# Statistical Model for Mobile User Positioning Based on Social Information
## 2 Related Work  
Today, mobile networks include very sophisticated terminal-based and network-based user positioning methods [9]. Classical network positioning methods can be based on or assisted by both the network and the user equipment (UE). To summarize the main positioning methods described in the bibliography, Table 1 presents a brief description of the advantages and inconveniences of each method. Currently, in LTE, the main positioning method is Assisted GPS (A-GPS) [10], in which the UE determines its location by processingGPS signals. This requires a line-of-sight connection to several satellites and knowledge of the orbital parameters. These data are downloaded before the first position fix, which can take several minutes (cold fix). To speed up the process, a network server can provide assisting data (e.g., precise timing, satellite Doppler shift, approximate position of the mobile device, or differential corrections) to obtain a faster and more reliable GPS fix (warm fix). When the GPS signal is not available (e.g., in urban or indoor environments), other positioning methods are used. In the main alternative method, the user's position can be derived by the network from a combination of the Cell ID (CID) and the uplink Timing Advance (TA) (also known as Enhanced CID (E-CID)), from which a server approximates the location of the equipment. Other methods employed are multilateration techniques based on the Angle of Arrival (AOA) or the Time Difference Of Arrival (TDOA), in which UE measures the arrival angle/time of positioning reference signals from multiple cells, from which a server obtains the user's position by multilateration [11]. Similarly, other techniques employ RSS measurements to perform distance estimation [12]. In contrast, mapping-based location methods capture signatures that are compared to a set of geotagged measurements to identify the location of a device [13]. These techniques, initially designed to locate the user on a 2D plane, have been extended with the introduction of 3D MIMO technology for 3D indoor scenarios [14]. In addition, the use of different sensors (e.g., barometric to determine indoor ground level [15], proximity [16], or inertial sensors [17]) has recently become more standardized. Position estimations with these techniques are affected by inaccuracies introduced by the radio channel (e.g., non-line-of-sight, shadows, multipath). To reduce their impact, data filtering algorithms (e.g., least-squares methods in overdetermined systems [11], Kalman filter [18], particle filter [19], or machine learning [20]) are employed in commercial GPS chipsets and positioning solutions. However, despite all positioning methods working in real-time, the information recorded for network management is very limited. This problem was partly solved with the MDT feature [21], whereby operators can request user equipment to report radio measurements along with location information. Unfortunately, MDT is rarely activated for all users and is continuous over time among other things, the battery consumption required by the user, which means that the anonymous call traces provided by network equipment often lack detailed location information. Therefore, network replanning and optimization must be done based on E-CID geolocated traces, with location errors of hundreds of meters, excessive for estimating the communication context. In parallel, interest in data science has been increasing in recent years due to recent advances in information technology. As a result, many open data initiatives have been launched around the world. Open data portals now offer direct and automated access to valuable assets that can be used to improve the management of cellular networks. Some companies (e.g., OpenSignal [22] or WeFi [23]) offer real metrics collected by anonymous (also known as crowdsourced) users, which can be used to evaluate current cellular deployments [8]. Social networks are gaining momentum in the interest of academia, due to the almost ubiquity of the service.