TY - GEN
T1 - Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks
AU - Al-Tahmeesschi, Ahmed
AU - Talvitie, Jukka
AU - Lopez-Benitez, Miguel
AU - Ruotsalainen, Laura
N1 - Funding Information:
This work was supported by the Academy of Finland Flagship programme: Finnish Center for Artificial Intelligence FCAI, Department of Computer Science, University of Helsinki.
Publisher Copyright:
© 2022 IEEE.
JUFOID=72237
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - 5G
KW - beamforming
KW - deep learning
KW - fingerprinting
KW - UE positioning
U2 - 10.1109/ICL-GNSS54081.2022.9797017
DO - 10.1109/ICL-GNSS54081.2022.9797017
M3 - Conference contribution
AN - SCOPUS:85134586031
SN - 9781665405768
T3 - International Conference on Localization and GNSS
BT - 2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings
A2 - Nurmi, Jari
A2 - Lohan, Elena-Simona
A2 - Sospedra, Joaquin Torres
A2 - Kuusniemi, Heidi
A2 - Ometov, Aleksandr
PB - IEEE
T2 - International Conference on Localization and GNSS
Y2 - 7 June 2022 through 9 June 2022
ER -