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ML-Enabled Outdoor ...
ML-Enabled Outdoor User Positioning in 5G NR Systems via Uplink SRS Channel Estimates
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- Ráth, Andre (författare)
- Lund University,Lunds universitet,MAX IV-laboratoriet,MAX IV Laboratory
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- Pjanić, Dino (författare)
- Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,LTH profilområde: AI och digitalisering,LTH profilområden,Lunds Tekniska Högskola,Communications Engineering,Lund University Research Groups,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH
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- Bernhardsson, Bo (författare)
- Lund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas
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- Tufvesson, Fredrik (författare)
- Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,LTH profilområde: AI och digitalisering,LTH profilområden,Lunds Tekniska Högskola,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Communications Engineering,Lund University Research Groups,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas
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Zorzi, Michele (redaktör/utgivare)
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Tao, Meixia (redaktör/utgivare)
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Saad, Walid (redaktör/utgivare)
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(creator_code:org_t)
- 2023
- 2023
- Engelska 6 s.
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Ingår i: ICC 2023 - IEEE International Conference on Communications : Sustainable Communications for Renaissance - Sustainable Communications for Renaissance. - 1550-3607. - 9781538674628 ; 2023-May, s. 2215-2220
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and reducing complexity. In this paper, we investigate ML techniques for positioning using 5G NR fingerprints consisting of uplink channel estimates from the physical layer channel. We show that it is possible to use Sounding Reference Signals (SRS) channel fingerprints to provide sufficient data to infer user position. Furthermore, we show that small fully-connected moderately Deep Neural Networks, even when applied to very sparse SRS data, can achieve successful outdoor user positioning with meter-level accuracy in a commercial 5G environment.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
Nyckelord
- 5G
- beamforming
- deep neural network
- localization
- machine learning
- positioning
- radio access network
- sounding reference signal
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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