Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G

Roman Klus; Jukka Talvitie; Mikko Valkama

doi:10.1109/ICL-GNSS51451.2021.9452245

Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

33 Citations (Scopus)

56 Downloads (Pure)

Abstract

In modern radio networks with large antenna arrays and precise beamforming techniques, accurate user positioning plays a key role in enabling seamless mobility management, link optimization, navigation and safety control. In open and rural areas, Global Navigation Satellite Systems (GNSS) are able to provide high-accuracy and high-reliability positioning performance. However, in urban and densely built-up areas the GNSS performance is typically substantially degraded due to rich scattering and multipath propagation effects. In this paper, we propose a machine learning based solution to boost positioning accuracy in urban areas by (i) obtaining User Equipment (UE) position from beamformed Radio Signal Strength (RSS) measurements and (ii) coherently fusing it with GNSS-based positioning data to enhance overall positioning performance. Based on the obtained numerical results, we were able to achieve a meter-level accuracy with the proposed machine learning model utilizing the beamformed RSS measurements, and subsequently improve the positioning accuracy further via fusion with GNSS data.

Original language	English
Title of host publication	2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings
Editors	Jari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781728196442
ISBN (Print)	9781728196459
DOIs	https://doi.org/10.1109/ICL-GNSS51451.2021.9452245
Publication status	Published - 2021
Publication type	A4 Article in conference proceedings
Event	International Conference on Localization and GNSS - Tampere, Finland Duration: 1 Jun 2021 → 3 Jun 2021

Publication series

Name	International Conference on Localization and GNSS
ISSN (Print)	2325-0747
ISSN (Electronic)	2325-0771

Conference

Conference	International Conference on Localization and GNSS
Country/Territory	Finland
City	Tampere
Period	1/06/21 → 3/06/21

Funding

The authors would like to thank the Academy of Finland (grants #323244 and #319994) for their support of this work.

Keywords

5G
beamforming
deep learning
fingerprinting
positioning
sensor fusion

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Computer Networks and Communications
Aerospace Engineering
Control and Optimization

Access to Document

10.1109/ICL-GNSS51451.2021.9452245

Neural Network Fingerprinting and GNSS Data
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Accepted author manuscript, 7.09 MB

http://urn.fi/URN:NBN:fi:tuni-202109297324

Cite this

Klus, R., Talvitie, J., & Valkama, M. (2021). Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G. In J. Nurmi, E.-S. Lohan, J. Torres-Sospedra, H. Kuusniemi, & A. Ometov (Eds.), 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings Article 9452245 (International Conference on Localization and GNSS). IEEE. https://doi.org/10.1109/ICL-GNSS51451.2021.9452245

Klus, Roman ; Talvitie, Jukka ; Valkama, Mikko. / Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G. 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings. editor / Jari Nurmi ; Elena-Simona Lohan ; Joaquin Torres-Sospedra ; Heidi Kuusniemi ; Aleksandr Ometov. IEEE, 2021. (International Conference on Localization and GNSS).

@inproceedings{117a282e3bb34af59f7949c6def8c4d1,

title = "Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G",

abstract = "In modern radio networks with large antenna arrays and precise beamforming techniques, accurate user positioning plays a key role in enabling seamless mobility management, link optimization, navigation and safety control. In open and rural areas, Global Navigation Satellite Systems (GNSS) are able to provide high-accuracy and high-reliability positioning performance. However, in urban and densely built-up areas the GNSS performance is typically substantially degraded due to rich scattering and multipath propagation effects. In this paper, we propose a machine learning based solution to boost positioning accuracy in urban areas by (i) obtaining User Equipment (UE) position from beamformed Radio Signal Strength (RSS) measurements and (ii) coherently fusing it with GNSS-based positioning data to enhance overall positioning performance. Based on the obtained numerical results, we were able to achieve a meter-level accuracy with the proposed machine learning model utilizing the beamformed RSS measurements, and subsequently improve the positioning accuracy further via fusion with GNSS data. ",

keywords = "5G, beamforming, deep learning, fingerprinting, positioning, sensor fusion",

author = "Roman Klus and Jukka Talvitie and Mikko Valkama",

note = "JUFOID=72237 Funding Information: The authors would like to thank the Academy of Finland (grants \#323244 and \#319994) for their support of this work. Publisher Copyright: {\textcopyright} 2021 IEEE.; International Conference on Localization and GNSS ; Conference date: 01-06-2021 Through 03-06-2021",

year = "2021",

doi = "10.1109/ICL-GNSS51451.2021.9452245",

language = "English",

isbn = "9781728196459",

series = "International Conference on Localization and GNSS",

publisher = "IEEE",

editor = "Jari Nurmi and Elena-Simona Lohan and Joaquin Torres-Sospedra and Heidi Kuusniemi and Aleksandr Ometov",

booktitle = "2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings",

address = "United States",

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Klus, R , Talvitie, J & Valkama, M 2021, Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G. in J Nurmi, E-S Lohan, J Torres-Sospedra, H Kuusniemi & A Ometov (eds), 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings., 9452245, International Conference on Localization and GNSS, IEEE, International Conference on Localization and GNSS, Tampere, Finland, 1/06/21. https://doi.org/10.1109/ICL-GNSS51451.2021.9452245

Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G. / Klus, Roman ; Talvitie, Jukka ; Valkama, Mikko.
2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings. ed. / Jari Nurmi; Elena-Simona Lohan; Joaquin Torres-Sospedra; Heidi Kuusniemi; Aleksandr Ometov. IEEE, 2021. 9452245 (International Conference on Localization and GNSS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G

AU - Klus, Roman

AU - Talvitie, Jukka

AU - Valkama, Mikko

PY - 2021

Y1 - 2021

N2 - In modern radio networks with large antenna arrays and precise beamforming techniques, accurate user positioning plays a key role in enabling seamless mobility management, link optimization, navigation and safety control. In open and rural areas, Global Navigation Satellite Systems (GNSS) are able to provide high-accuracy and high-reliability positioning performance. However, in urban and densely built-up areas the GNSS performance is typically substantially degraded due to rich scattering and multipath propagation effects. In this paper, we propose a machine learning based solution to boost positioning accuracy in urban areas by (i) obtaining User Equipment (UE) position from beamformed Radio Signal Strength (RSS) measurements and (ii) coherently fusing it with GNSS-based positioning data to enhance overall positioning performance. Based on the obtained numerical results, we were able to achieve a meter-level accuracy with the proposed machine learning model utilizing the beamformed RSS measurements, and subsequently improve the positioning accuracy further via fusion with GNSS data.

AB - In modern radio networks with large antenna arrays and precise beamforming techniques, accurate user positioning plays a key role in enabling seamless mobility management, link optimization, navigation and safety control. In open and rural areas, Global Navigation Satellite Systems (GNSS) are able to provide high-accuracy and high-reliability positioning performance. However, in urban and densely built-up areas the GNSS performance is typically substantially degraded due to rich scattering and multipath propagation effects. In this paper, we propose a machine learning based solution to boost positioning accuracy in urban areas by (i) obtaining User Equipment (UE) position from beamformed Radio Signal Strength (RSS) measurements and (ii) coherently fusing it with GNSS-based positioning data to enhance overall positioning performance. Based on the obtained numerical results, we were able to achieve a meter-level accuracy with the proposed machine learning model utilizing the beamformed RSS measurements, and subsequently improve the positioning accuracy further via fusion with GNSS data.

KW - 5G

KW - beamforming

KW - deep learning

KW - fingerprinting

KW - positioning

KW - sensor fusion

U2 - 10.1109/ICL-GNSS51451.2021.9452245

DO - 10.1109/ICL-GNSS51451.2021.9452245

M3 - Conference contribution

AN - SCOPUS:85112867042

SN - 9781728196459

T3 - International Conference on Localization and GNSS

BT - 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings

A2 - Nurmi, Jari

A2 - Lohan, Elena-Simona

A2 - Torres-Sospedra, Joaquin

A2 - Kuusniemi, Heidi

A2 - Ometov, Aleksandr

PB - IEEE

T2 - International Conference on Localization and GNSS

Y2 - 1 June 2021 through 3 June 2021

ER -

Klus R , Talvitie J , Valkama M. Neural Network Fingerprinting and GNSS Data Fusion for Improved Localization in 5G. In Nurmi J, Lohan ES, Torres-Sospedra J, Kuusniemi H, Ometov A, editors, 2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings. IEEE. 2021. 9452245. (International Conference on Localization and GNSS). doi: 10.1109/ICL-GNSS51451.2021.9452245