Experimental Study of Telco Localization Methods

Telecommunication (Telco) localization is a technique to accurately locate mobile devices (MDs) using measurement report (MR) data, and has been widely used in Telco industry. Many techniques have been proposed, including measurement-based statistical algorithms, fingerprinting algorithms and different machine learning-based algorithms. However, it has not been well studied yet on how these algorithms perform on various Telco MR data sets. In this paper, we conduct a comprehensive experimental study of five state-of-art algorithms for Telco localization. Based on real data sets from two Telco networks, we study the localization performance of such algorithms. We find that a Random Forest-based machine learning algorithm performs best in most experiments due to high localization accuracy and insensitivity to data volume. The experimental result and observation in this paper may inspire and enhance future research in Telco localization.