3D UAV trajectory and data collection optimisation via deep reinforcement learning
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Accepted version
Date
2022-04
Authors
Nguyen, Khoi Khac
Duong, Trung Q.
Do-Duy, Tan
Claussen, Holger
Hanzo, Llajos
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Published Version
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.
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Keywords
Data collection , Data collection , Deep reinforcement learning , Optimization , Resource management , Three-dimensional displays , Throughput , Trajectory , Trajectory , UAV-assisted wireless network , Wireless networks
Citation
Nguyen, K. K., Duong, T. Q., Do-Duy, T., Claussen, H. and Hanzo, L. (2022) '3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning', IEEE Transactions On Communications, 70 (4), pp. 2358-2371. doi: 10.1109/TCOMM.2022.3148364
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