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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...

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Detalles Bibliográficos
Autores principales: Bithas, Petros S., Michailidis, Emmanouel T., Nomikos, Nikolaos, Vouyioukas, Demosthenes, Kanatas, Athanasios G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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.
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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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