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Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users
A network composed of unmanned aerial vehicles (UAVs), serving as base stations (UAV-BS network), is emerging as a promising component in next-generation communication systems. In the UAV-BS network, the optimal positioning of a UAV-BS is an essential requirement to establish line-of-sight (LoS) lin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708867/ https://www.ncbi.nlm.nih.gov/pubmed/34960332 http://dx.doi.org/10.3390/s21248239 |
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author | Lee, Wonseok Jeon, Young Kim, Taejoon Kim, Young-Il |
author_facet | Lee, Wonseok Jeon, Young Kim, Taejoon Kim, Young-Il |
author_sort | Lee, Wonseok |
collection | PubMed |
description | A network composed of unmanned aerial vehicles (UAVs), serving as base stations (UAV-BS network), is emerging as a promising component in next-generation communication systems. In the UAV-BS network, the optimal positioning of a UAV-BS is an essential requirement to establish line-of-sight (LoS) links for ground users. A novel deep Q-network (DQN)-based learning model enabling the optimal deployment of a UAV-BS is proposed. Moreover, without re-learning of the model and the acquisition of the path information of ground users, the proposed model presents the optimal UAV-BS trajectory while ground users move. Specifically, the proposed model optimizes the trajectory of a UAV-BS by maximizing the mean opinion score (MOS) for ground users who move to various paths. Furthermore, the proposed model is highly practical because, instead of the locations of individual mobile users, an average channel power gain is used as an input parameter. The accuracy of the proposed model is validated by comparing the results of the model with those of a mathematical optimization solver. |
format | Online Article Text |
id | pubmed-8708867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87088672021-12-25 Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users Lee, Wonseok Jeon, Young Kim, Taejoon Kim, Young-Il Sensors (Basel) Communication A network composed of unmanned aerial vehicles (UAVs), serving as base stations (UAV-BS network), is emerging as a promising component in next-generation communication systems. In the UAV-BS network, the optimal positioning of a UAV-BS is an essential requirement to establish line-of-sight (LoS) links for ground users. A novel deep Q-network (DQN)-based learning model enabling the optimal deployment of a UAV-BS is proposed. Moreover, without re-learning of the model and the acquisition of the path information of ground users, the proposed model presents the optimal UAV-BS trajectory while ground users move. Specifically, the proposed model optimizes the trajectory of a UAV-BS by maximizing the mean opinion score (MOS) for ground users who move to various paths. Furthermore, the proposed model is highly practical because, instead of the locations of individual mobile users, an average channel power gain is used as an input parameter. The accuracy of the proposed model is validated by comparing the results of the model with those of a mathematical optimization solver. MDPI 2021-12-09 /pmc/articles/PMC8708867/ /pubmed/34960332 http://dx.doi.org/10.3390/s21248239 Text en © 2021 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 | Communication Lee, Wonseok Jeon, Young Kim, Taejoon Kim, Young-Il Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users |
title | Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users |
title_full | Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users |
title_fullStr | Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users |
title_full_unstemmed | Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users |
title_short | Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users |
title_sort | deep reinforcement learning for uav trajectory design considering mobile ground users |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708867/ https://www.ncbi.nlm.nih.gov/pubmed/34960332 http://dx.doi.org/10.3390/s21248239 |
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