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AuthorDabiri, Mohammad Taghi
AuthorHasna, Mazen
Available date2024-06-11T04:44:11Z
Publication Date2023
Publication NameIEEE Vehicular Technology Conference
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/VTC2023-Spring57618.2023.10199600
ISSN1550-2252
URIhttp://hdl.handle.net/10576/56003
AbstractAs an alternative solution for quick disaster recovery of backhaul/fronthaul links, in this paper, a dynamic unmanned aerial vehicles (UAV)-assisted heterogeneous (HetNet) network equipped with directional terahertz (THz) antennas is studied to solve the problem of transferring traffic of distributed small cells. To this end, we first characterize a detailed three-dimensional modeling of the dynamic UAV-assisted HetNet, and then, we formulate the problem for UAV trajectory to minimize the maximum outage probability of directional THz links. Then, using deep reinforcement learning (DRL) method, we propose an efficient algorithm to learn the optimal trajectory. Finally, using simulations, we investigate the performance of the proposed DRL-based trajectory method.
SponsorThis publication was made possible by grant number NPRP13S-0130-200200 from the Qatar National Research Fund, QNRF. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherIEEE
SubjectAntenna pattern
deep reinforcement learning
THz
trajectory
UAV
TitleUAV Trajectory Optimization for Directional THz Links Using Deep Reinforcement Learning
TypeConference Paper
Volume Number2023-June
dc.accessType Abstract Only


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