Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks

Abstract

Outdoor user equipment (DE) localisation has attracted a significant amount of attention due to its importance in many location-based services. Typically, in rural and open areas, global navigation satellite systems (GNSS) can provide an accurate and reliable localisation performance. However, in urban areas GNSS localisation accuracy is significantly reduced due to shadowing, scattering and signal blockages. In this work, the UE positioning assisted by deep learning in 5G and beyond networks is investigated in an urban area environment. We study the impact of utilising the spatial correlation in the received signal strengths (RSSs) on the UE positioning accuracy and how to utilise such correlation with deep learning algorithms to improve the localisation accuracy. Numerical results showed the importance of utilising the spatial correlation in the RSS to improve the prediction accuracy for all of the considered models. In addition, the impact of varying the number of access points (APs) transmitters on the localisation accuracy is also investigated. Numerical results showed that a lower number of APs may be sufficient when not considering uncertainties in RSS measurements. Moreover, we study how much the degrading effect of RSS uncertainty can be compensated for by increasing the number of APs.