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5G Positioning - A Machine Learning Approach
Linköping University, Department of Electrical Engineering, Automatic Control.ORCID iD: 0000-0003-0695-0720
Linköping University, Department of Electrical Engineering, Automatic Control.
Ericsson Research, Sweden.
Ericsson Research, Sweden.
Show others and affiliations
2019 (English)In: 2019 16th Workshop on Positioning, Navigation and Communications (WPNC), 2019Conference paper, Published paper (Refereed)
Abstract [en]

In urban environments, cellular network-based positioning of user equipment (UE) is a challenging task, especially in frequently occurring non-line-of-sight (NLOS) conditions. This paper investigates the use of two machine learning methods – neural networks and random forests – to estimate the position of UE in NLOS using best received reference signal beam power measurements. We evaluated the suggested positioning methods using data collected from a fifth-generation cellular network (5G) testbed provided by Ericsson. A statistical test to detect NLOS conditions with a probability of detection that is close to 90% is suggested. We show that knowledge of the antenna are crucial for accurate position estimation. In addition, our results show that even with a limited set of training data and one 5G transmission point, it is possible to position UE within 10 meters with 80% accuracy.

Place, publisher, year, edition, pages
2019.
Keywords [en]
5G cellular networks, positioning, neural networks, random forest, nlos conditions
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-161450OAI: oai:DiVA.org:liu-161450DiVA, id: diva2:1367121
Conference
2019 16th Workshop on Positioning, Navigation and Communications (WPNC)
Available from: 2019-11-01 Created: 2019-11-01 Last updated: 2019-11-04

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
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  • en-US
  • fi-FI
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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