# Statistical Model for Mobile User Positioning Based on Social Information
## 3 Methodology  
The knowledge of users aggregations areas that could, for example, represent a bottleneck in the network is key for operators management tasks. Unfortunately, the only way for terminals to report users' position coordinates to the OSS is to activate the MDT functionality, but it is not usually active due to the resource consumption involved, so the GPS position of users in the network is rarely known. However, from a mobile network management point of view, it is unnecessary to know the precise position of each UE in the scenario, but it is essential to know their statistical distribution. E-CID can be a decent approach to the problem of approximating the users' position, mainly in rings close to the \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantage** & **Inconvenience** \\ \hline A-GPS & High accuracy in positioning. & Information usually not available for network operators. \\ \hline Enhanced CID & Great simplicity, based on network measurements. & Low accuracy, decreasing with the distance to the node. \\ \hline \multirow{3}{*}{Lateration} & Higher accuracy than E- CID. Based on network measurements. & Requires equipment features not typically available. Vulnerable to propagation conditions. \\ \hline \multirow{3}{*}{Fingerprinting} & Localization with simpler measurements. & Great complexity when calibrating maps due to the issues in obtaining measurements. Vulnerable to propagation conditions variations. \\ \hline \multirow{3}{*}{Minimization Drive Test} & Communicate GPS position to network operators. Enriched with network measurements to ease maps construction for fingerprinting techniques & High consumption of resources, such as terminals’ batteries. \\ \hline \multirow{3}{*}{Proposed model} & Simplicity equivalent to enhanced CID method with much higher accuracy. Less complex map calibration comparing to fingerprinting, due to the access of external data. \\ \cline{1-1}  & & Dependent on the availability of external data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Positioning techniques. serving base station due to the small coverage area, but for other rings, the coverage area is too wide to determine the UE location properly, thus improving the method is key to optimize position-based processes. One way to reduce this imprecision in the localization process is to add information about the users' context. In particular, users' location in indoor/outdoor environments is especially interesting, locating the user in the buildings served by the ring or in the streets/parks in that area, thus greatly reducing the area of imprecision. On the other hand, social applications are in the spotlight, leading users to generate a constant traffic flow, sharing their experiences and impressions with the rest of the world. In this situation, social networks play a fundamental role, providing the general public with access to a portal where they can publish their ideas, photos, or videos on a massive scale and, in many cases (such as Twitter or Flickr), allowing the addition of metadata about the place, as the GPS coordinates where the publication was created. Thus, the inclusion of context information, as well as other information from outside the mobile network (e.g., geolocated data from social networks), is of great interest to provide granularity to the process of spatial distribution of users, thus increasing its overall accuracy and, mainly, in distant rings. In order to estimate where the connections are located in the scenario, inputs described above must first be preprocessed. In this section, the processing applied to the input data is first described to learn the spatial characteristics of the scenario. Later, the process of statistical distribution of connections is explained.