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dc.contributor.authorChang, Zheng
dc.contributor.authorDeng, Hengwei
dc.contributor.authorYou, Li
dc.contributor.authorMin, Geyong
dc.contributor.authorGarg, Sahil
dc.contributor.authorKaddoum, Georges
dc.date.accessioned2022-05-11T11:00:59Z
dc.date.available2022-05-11T11:00:59Z
dc.date.issued2022
dc.identifier.citationChang, Z., Deng, H., You, L., Min, G., Garg, S., & Kaddoum, G. (2022). Trajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches. <i>IEEE Transactions on Network Science and Engineering</i>, <i>Early Access</i>. <a href="https://doi.org/10.1109/tnse.2022.3171600" target="_blank">https://doi.org/10.1109/tnse.2022.3171600</a>
dc.identifier.otherCONVID_144231417
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/81007
dc.description.abstractThe future mobile communication system is expected to provide ubiquitous connectivity and unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which is prominent in its flexibility and low cost, emerges as a significant network entity to realize such ambitious targets. In this work, novel machine learning-based trajectory design and resource allocation schemes are presented for a multi-UAV communications system. In the considered system, the UAVs act as aerial Base Stations (BSs) and provide ubiquitous coverage. In particular, with the objective to maximize the system utility over all served users, a joint user association, power allocation and trajectory design problem is presented. To solve the problem caused by high dimensionality in state space, we first propose a machine learning-based strategic resource allocation algorithm which comprises of reinforcement learning and deep learning to design the optimal policy of all the UAVs. Then, we also present a multi-agent deep reinforcement learning scheme for distributed implementation without knowing a priori knowledge of the dynamic nature of networks. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Transactions on Network Science and Engineering
dc.rightsCC BY 4.0
dc.subject.otherresource management
dc.subject.othertrajectory
dc.subject.otherautonomous aerial vehicles
dc.subject.othercommunication systems
dc.subject.otherreinforcement learning
dc.subject.otherwireless networks
dc.subject.otherthroughput
dc.titleTrajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202205112656
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingfi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineOhjelmisto- ja tietoliikennetekniikkafi
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingen
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineSoftware and Communications Engineeringen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn2327-4697
dc.relation.volumeEarly Access
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysolangattomat verkot
dc.subject.ysomiehittämättömät ilma-alukset
dc.subject.ysosyväoppiminen
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p24221
jyx.subject.urihttp://www.yso.fi/onto/yso/p24149
jyx.subject.urihttp://www.yso.fi/onto/yso/p39324
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1109/tnse.2022.3171600
jyx.fundinginformationThis work is partly supported by the National Natural Science Foundation of China (NSFC) under Grant 62071105. The work of Li You was supported in part by the Young Elite Scientist Sponsorship Program by China Institute of Communications, the Jiangsu Province Basic Research Project under Grant BK20192002, and the Fundamental Research Funds for the Central Universities.


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