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- W4286572053 abstract "The development of the fifth-generation (5G) communication technology and various emerging Internet of Things (IoT) applications have brought in great challenge of seamless connectivity for massive IoT devices. Recently, unmanned aerial vehicle (UAV) has been regarded as a promising solution to improve network coverage and energy efficiency for IoT devices in disaster or remote areas. Under this circumstance, how to minimize the data transmission delay with respect to the limited UAV energy is crucial. Thus we in this paper try to solve the problem by optimizing the UAV trajectory. Specifically, we first formulate the device grouping issue as a maximum clique problem, which is solved by an ant colony algorithm. Then, a Q-learning algorithm is proposed to solve the UAV trajectory planning problem. Simulation results show that the proposed algorithms can yield a globally optimal solution, and outperform the existing K-means and NTE based algorithms." @default.
- W4286572053 created "2022-07-22" @default.
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- W4286572053 date "2022-06-06" @default.
- W4286572053 modified "2023-09-26" @default.
- W4286572053 title "Trajectory Planning in UAV-Assisted Wireless Networks via Reinforcement Learning" @default.
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- W4286572053 doi "https://doi.org/10.1109/hpsr54439.2022.9831330" @default.
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