UAV trajectory optimization based on predicted user locations

Thumbnail Image
Files
Date
2024-07-03
Authors
Ho, Lester
Jangsher, Sobia
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
https://doi.org/10.1109/WCNC57260.2024.10570825
Research Projects
Organizational Units
Journal Issue
Abstract
Unmanned aerial vehicles (UAVs) can extend the coverage of wireless networks due to their high mobility and the favorable radio propagation characteristics. This paper studies the trajectory optimization of UAV that are acting as radio relays. The optimization is based on predicted user locations (UTO-PUL) to assist communication to the ground users who are unable to get coverage from the base station (BS). The existing work on trajectory design has considered several optimization approaches as well as reinforcement learning (RL) algorithms. All the algorithm takes into consideration the existing state of the network such as the channel conditions, initial positions and computes the destination of the UAV based on it. The proposed algorithm is designed to also consider the predicted user mobility of the future instances. The objective is to ensure the ground users are connected to the BS. The proposed UTO-PUL algorithm's performance is evaluated using simulations in a scenario with challenging terrain, where the proposed algorithm reduced the probability of users having no coverage by between 45% to 85% compared to non-predictive approaches, and achieved gains in median downlink signal power of 14 dB compared with a deep reinforcement learning (DRL) algorithm.
Description
Keywords
Unmanned aerial vehicle (UAV) , Trajectory optimization , Drone , Prediction , Predicted user locations
Citation
Ho, L. and Jangsher, S. (2024) ‘UAV trajectory optimization based on predicted user locations’, 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, 21-24 April 2024, pp. 1–6. https://doi.org/10.1109/WCNC57260.2024.10570825
Link to publisher’s version
https://ieeexplore.ieee.org/document/10570825
URI
https://hdl.handle.net/10468/16837
Collections
Tyndall National Institute - Conference Items
Copyright
© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.