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

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

8 Lataukset (Pure)


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.

Otsikko2021 International Conference on Localization and GNSS, ICL-GNSS 2021 - Proceedings
ToimittajatJari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov
ISBN (elektroninen)9781728196442
ISBN (painettu)9781728196459
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Localization and GNSS - Tampere, Suomi
Kesto: 1 kesäk. 20213 kesäk. 2021


NimiInternational Conference on Localization and GNSS
ISSN (painettu)2325-0747
ISSN (elektroninen)2325-0771


ConferenceInternational Conference on Localization and GNSS


  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Computer Networks and Communications
  • Aerospace Engineering
  • Control and Optimization


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