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Positioning in 5G and 6G Networks—A Survey
Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268850/ https://www.ncbi.nlm.nih.gov/pubmed/35808254 http://dx.doi.org/10.3390/s22134757 |
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author | Mogyorósi, Ferenc Revisnyei, Péter Pašić, Azra Papp, Zsófia Törös, István Varga, Pál Pašić, Alija |
author_facet | Mogyorósi, Ferenc Revisnyei, Péter Pašić, Azra Papp, Zsófia Törös, István Varga, Pál Pašić, Alija |
author_sort | Mogyorósi, Ferenc |
collection | PubMed |
description | Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios. |
format | Online Article Text |
id | pubmed-9268850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92688502022-07-09 Positioning in 5G and 6G Networks—A Survey Mogyorósi, Ferenc Revisnyei, Péter Pašić, Azra Papp, Zsófia Törös, István Varga, Pál Pašić, Alija Sensors (Basel) Review Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios. MDPI 2022-06-23 /pmc/articles/PMC9268850/ /pubmed/35808254 http://dx.doi.org/10.3390/s22134757 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Mogyorósi, Ferenc Revisnyei, Péter Pašić, Azra Papp, Zsófia Törös, István Varga, Pál Pašić, Alija Positioning in 5G and 6G Networks—A Survey |
title | Positioning in 5G and 6G Networks—A Survey |
title_full | Positioning in 5G and 6G Networks—A Survey |
title_fullStr | Positioning in 5G and 6G Networks—A Survey |
title_full_unstemmed | Positioning in 5G and 6G Networks—A Survey |
title_short | Positioning in 5G and 6G Networks—A Survey |
title_sort | positioning in 5g and 6g networks—a survey |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268850/ https://www.ncbi.nlm.nih.gov/pubmed/35808254 http://dx.doi.org/10.3390/s22134757 |
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