Confidence Model-Based Data Repair for Telco Localization

Abstract

Telecommunication (Telco) localization is a technique to localize mobile devices (MDs) outdoor by using measurement report (MR) data. Unfortunately, existing Telco localization approaches (with localization error > 50 meters) cannot achieve comparable localization accuracy as GPS (with localization error ±10 meters). In particular, due to signal interference and attenuation caused by high buildings in urban cities, it is hard to achieve high localization accuracy if MR records contain unstable signal data. To this end, we propose to first detect those MR records incurring high localization errors and next repair the predicted location (with high errors). Our experiments on two real MR data sets from 2G GSM and 4G LTE Telco networks verify that a Telco localization approach, enhanced by the proposed detection and repair algorithms, can greatly improve localization accuracy. For example, the enhancement algorithm on 2G and 4G data sets can achieve 29.5 and 13.2 meters of median errors, around 160.68% and 201.51% better than previous results. Such result indicates that our work can achieve nearly comparable localization accuracy as GPS.