3D UAV trajectory and data collection optimisation via deep reinforcement learning

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dc.contributor.authorNguyen, Khoi Khac
dc.contributor.authorDuong, Trung Q.
dc.contributor.authorDo-Duy, Tan
dc.contributor.authorClaussen, Holger
dc.contributor.authorHanzo, Llajos
dc.contributor.funderRoyal Academy of Engineeringen
dc.contributor.funderEuropean Research Councilen
dc.contributor.funderEngineering and Physical Sciences Research Councilen
dc.date.accessioned2022-04-27T15:10:42Z
dc.date.available2022-04-27T15:10:42Z
dc.date.issued2022-04
dc.date.updated2022-04-27T15:00:06Z
dc.description.abstractUnmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on- board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.en
dc.description.sponsorshipU.K. Royal Academy of Engineering (RAEng Research Chair and Senior Research Fellowship scheme Grant RCSRF2021\11\41); Engineering and Physical Sciences Research Counc (projects EP/P034284/1 and EP/P003990/1 (COALESCE); European Research Council (ERC Advanced Fellow Grant Quant-Com (Grant No. 789028))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationNguyen, K. K., Duong, T. Q., Do-Duy, T., Claussen, H. and Hanzo, L. (2022) '3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning', IEEE Transactions On Communications, 70 (4), pp. 2358-2371. doi: 10.1109/TCOMM.2022.3148364en
dc.identifier.doi10.1109/TCOMM.2022.3148364en
dc.identifier.endpage2371en
dc.identifier.issn0090-6778
dc.identifier.issued4en
dc.identifier.journaltitleIEEE Transactions On Communicationsen
dc.identifier.startpage2358en
dc.identifier.urihttps://hdl.handle.net/10468/13127
dc.identifier.volume70en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::ERC::ERC-ADG/789028/EU/Ubiquitous Quantum Communications/QuantComen
dc.relation.urihttps://ieeexplore.ieee.org/document/9701330
dc.rights© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen
dc.subjectData collectionen
dc.subjectData collectionen
dc.subjectDeep reinforcement learningen
dc.subjectOptimizationen
dc.subjectResource managementen
dc.subjectThree-dimensional displaysen
dc.subjectThroughputen
dc.subjectTrajectoryen
dc.subjectTrajectoryen
dc.subjectUAV-assisted wireless networken
dc.subjectWireless networksen
dc.title3D UAV trajectory and data collection optimisation via deep reinforcement learningen
dc.typeArticle (peer-reviewed)en
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