# Statistical Model for Mobile User Positioning Based on Social Information
###### Abstract  
In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems pinpointing problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the scenario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from data traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged posts from social networks collected in real-time. mobile network; localization; context; social networks; big data +
Footnote †: journal: Medical Informatics  
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To meet the ambitious specifications of the new use cases, 5G operators will take network performance to the next level by combining multiple techniques. For example, network densification has been recognized as an efficient way to provide higher network capacity and better coverage, combining macrocellular infrastructure with Small Cell (SC) deployments. In these heterogeneous networks, discovering the best locations for new SCs is the key to making the most of the new infrastructure. However, most current site selection approaches are only based on signal quality indicators, due to the difficulty of modeling the dynamic performance of the network. The Minimization Drive Test (MDT) function can mitigate this difficulty, enabling the collection of geolocated measurements that can be used to build accurate network performance maps (Radio Environment Map (REM)). These maps can be used to detect coverage gaps [7]. Unfortunately, MDT is rarely activated in live networks due to the workload involved in processing these measurements. Therefore, network organizing tasks often have to be performed based on measurements only positioned by cell identifier and Timing Advance (TA) statistics. This approach leads to large localization errors, even more in areas with high TA, where the combination of cell identifier and TA produces a large area location ring, which prevents estimating the user context. With recent advances in technology, which allows efficient processing of the vast amount of continuously generated information, interest in data science has grown in both the scientific and technical fields. As a result, many open data portals now offer direct and automated access to valuable assets that can be used to improve cellular network management. Some companies offer real metrics collected by anonymous users, which can be used to evaluate current deployments [8]. In contrast, other companies and some open data portals, usually governmental in nature, provide information on the main socio-economic activity occurring in each area of land (referred to in the literature as land use), which greatly influences how each person uses the network. The indoor context of users is a key factor in cellular network maintenance and management tasks. It can be applied to several types of problems, such as connection classification for the application of different context-aware QoE models or the optimization of geolocation methods in the mobile network. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the scenario, instead of other classical fingerprinting or triangulation positioning methods.