Abstract
Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.
Original language | English |
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Number of pages | 14 |
Journal | IEEE Transactions on Communications |
Early online date | 01 Feb 2022 |
DOIs | |
Publication status | Early online date - 01 Feb 2022 |
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Dive into the research topics of '3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning'. Together they form a unique fingerprint.Student theses
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Reconfigurable intelligent surface and UAV-assisted communications: A deep reinforcement learning approach
Nguyen, K. K. (Author), Duong, Q. (Supervisor), Jul 2022Student thesis: Doctoral Thesis › Doctor of Philosophy
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