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Adaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach

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dc.contributor.authorFonseca, Erikaen
dc.contributor.authorGalkin, Borisen
dc.contributor.authorAmer, Ramyen
dc.contributor.authorDaSilva, Luiz A.en
dc.contributor.authorDusparic, Ivanaen
dc.date.accessioned2023-01-27T20:55:51Zen
dc.date.available2023-01-27T20:55:51Zen
dc.date.issued2023-01-19en
dc.date.updated2023-01-27T20:35:10Zen
dc.description.abstractProviding reliable connectivity to cellular-connected Unmanned Aerial Vehicles (UAVs) can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground Base Stations (BSs). On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a Reinforcement Learning (RL) algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or spectrum ef ciency that it experiences. The proposed solution is evaluated in two settings: using a series of generated environments where we vary the number of BS and building densities, and in a scenario using real-world data obtained from an experiment in Dublin, Ireland. Results show that our proposed RL-based solution improves UAV Quality of Service (QoS) by 6% to 41%, depending on the scenario. We also conclude that, when ying at heights higher than the buildings, building density variation has no impact on UAV QoS. On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.en
dc.description.versionPublished versionen
dc.format.extentPages 5966-5980en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2022.3232077en
dc.identifier.eissn2169-3536en
dc.identifier.issn2169-3536en
dc.identifier.orcidPereira da Silva, Luiz [0000-0001-6310-6150]en
dc.identifier.urihttp://hdl.handle.net/10919/113548en
dc.identifier.volume11en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleAdaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approachen
dc.title.serialIEEE Accessen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutesen

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