3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning

Khoi Khac Nguyen, Trung Q. Duong, Tan Do-Duy, Holger Claussen, Lajos Hanzo

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)
738 Downloads (Pure)

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 languageEnglish
Number of pages14
JournalIEEE Transactions on Communications
Early online date01 Feb 2022
DOIs
Publication statusEarly online date - 01 Feb 2022

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