DeepMEND: Reliable and Scalable Network Metadata Geolocation from Base Station Positions

Abstract

Metadata geolocation, i.e., mapping information collected at a cellular Base Station (BS) to the geographical area it covers, is a central operation in the production of statistics from mobile network measurements. This task requires modeling the probability that a device attached to a BS is at a specific location, and is presently addressed with simplistic approximations based on Voronoi tessellations. As we show, Voronoi cells exhibit poor accuracy compared to real-world geolocation data, which can, in turn, reduce the reliability of research results. We propose a new approach for data-driven metadata geolocation based on a teacher-student paradigm that combines probabilistic inference and deep learning. Our DEEPMEND model: (i) only needs BS positions as input, exactly like Voronoi tessellations; (ii) produces geolocation maps that are 56% and 33% more accurate than legacy Voronoi and their state-of-the-art VoronoiBoost calibration, respectively; and, (iii) generates geolocation data for thousands of BSs in minutes. We assess its accuracy against real-world multi-city geolocation data of 5, 947 BSs provided by a network operator, and demonstrate the impact of its enhanced metadata geolocation on two applications use cases