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- W4205997974 abstract "Unmanned aerial vehicles (UAVs) arouse considerable interest in coverage applications such as emergency communication. This paper attempts to address the multi-UAV navigation problem for fair coverage in wireless networks, where a charging station is deployed to recharge the UAVs. It is challenging to control each UAV to provide coverage collaboratively such that each point-of-interest in the target area is covered fairly for a reasonable time duration. The problem of joint multi-UAV navigation and recharging is formulated into a Markov decision process with the objective to maximize the fair coverage score per unit of energy with recharging reward. The state-of-the-art deep reinforcement learning method, coined by us as PPO-UNC (proximal strategy optimization for multi- UAV navigation and recharging), is employed to find the solution efficiently. Simulation results show the superiority of the proposed PPO-UNC strategy as compared to two baseline policies." @default.
- W4205997974 created "2022-01-25" @default.
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- W4205997974 date "2021-11-26" @default.
- W4205997974 modified "2023-10-16" @default.
- W4205997974 title "Multi-UAV Navigation and Recharging for Fair and Sustainable Coverage in Wireless Networks" @default.
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- W4205997974 doi "https://doi.org/10.1145/3503047.3503129" @default.
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