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A Survey on Machine-Learning Techniques for UAV-Based Communications
Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new de...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929112/ https://www.ncbi.nlm.nih.gov/pubmed/31779133 http://dx.doi.org/10.3390/s19235170 |
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author | Bithas, Petros S. Michailidis, Emmanouel T. Nomikos, Nikolaos Vouyioukas, Demosthenes Kanatas, Athanasios G. |
author_facet | Bithas, Petros S. Michailidis, Emmanouel T. Nomikos, Nikolaos Vouyioukas, Demosthenes Kanatas, Athanasios G. |
author_sort | Bithas, Petros S. |
collection | PubMed |
description | Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security. |
format | Online Article Text |
id | pubmed-6929112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69291122019-12-26 A Survey on Machine-Learning Techniques for UAV-Based Communications Bithas, Petros S. Michailidis, Emmanouel T. Nomikos, Nikolaos Vouyioukas, Demosthenes Kanatas, Athanasios G. Sensors (Basel) Review Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security. MDPI 2019-11-26 /pmc/articles/PMC6929112/ /pubmed/31779133 http://dx.doi.org/10.3390/s19235170 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Bithas, Petros S. Michailidis, Emmanouel T. Nomikos, Nikolaos Vouyioukas, Demosthenes Kanatas, Athanasios G. A Survey on Machine-Learning Techniques for UAV-Based Communications |
title | A Survey on Machine-Learning Techniques for UAV-Based Communications |
title_full | A Survey on Machine-Learning Techniques for UAV-Based Communications |
title_fullStr | A Survey on Machine-Learning Techniques for UAV-Based Communications |
title_full_unstemmed | A Survey on Machine-Learning Techniques for UAV-Based Communications |
title_short | A Survey on Machine-Learning Techniques for UAV-Based Communications |
title_sort | survey on machine-learning techniques for uav-based communications |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929112/ https://www.ncbi.nlm.nih.gov/pubmed/31779133 http://dx.doi.org/10.3390/s19235170 |
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