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• W4382118119 abstract "We target the problem of deploying drone base stations (DBSs) to serve a set of users with known locations after dividing it into two problems: users clustering along with 2D localization of drones, and transmission power control. For clustering, we propose an algorithm inspired by the expectation-maximization (EM) algorithm that boosts link reliability. As for transmission power control, we utilize the Monte Carlo tree search (MCTS) algorithm. Furthermore, we show that the resulting mechanism can also adapt to user changes without rebuilding the search tree, and it also intrinsically avoids collision between DBSs. Also, our simulations show that our proposed algorithm improves the link reliability and system energy efficiency substantially in comparison to another mechanism mentioned in the literature. The results show that controlling the power of DBSs does not only reduce power consumption but also improves the users’ links quality. Finally, we show the improvement in system performance relative to the baseline method where DBSs are randomly distributed and users are changing their location according to the random waypoint mobility model." @default.
• W4382118119 created "2023-06-27" @default.
• W4382118119 creator A5006287085 @default.
• W4382118119 creator A5011947791 @default.
• W4382118119 date "2023-01-01" @default.
• W4382118119 modified "2023-09-24" @default.
• W4382118119 title "Drone Base Stations Transmission Power Control and Localization" @default.
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• W4382118119 doi "https://doi.org/10.1007/978-3-031-34776-4_19" @default.
• W4382118119 hasPublicationYear "2023" @default.
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