Metaheuristic Optimization of Trajectory and Dynamic Time Splitting for UAV Communication Systems

The integration of unmanned aerial vehicles (UAVs) into wireless communication systems has emerged as a transformative approach, promising cost-efficient connectivity. This letter addresses the optimization of the dynamic time-splitting ratio and flight trajectory for a communication system linking a ground base station to the UAV equipped with backscatter devices (referred to as UB), and from UB to an end user. Given the inherent non-convexity of the problem, we develop two meta-heuristic-based approaches inspired by genetic algorithm and particle swarm optimization to enhance the total achievable rate while reducing computational complexity. Numerical results demonstrate the effectiveness of these meta-heuristic solutions, showcasing significant improvements in the achievable rate and computation time compared to existing benchmarks.

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Publication Details

Journal
IEEE Communications Letters
Published
2025-04-01
DOI
https://doi.org/10.1109/lcomm.2025.3556714
Primary Topic
Satellite Communication Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Metaheuristic Optimization of Trajectory and Dynamic Time Splitting for UAV Communication Systems

Trinh Van Chien, Van‐Dinh Nguyen, Oh‐Soon Shin, Nguyễn Minh Quân
IEEE Communications Letters
Satellite Communication Systems
article

Metaheuristic Optimization of Trajectory and Dynamic Time Splitting for UAV Communication Systems

Trinh Van Chien, Van‐Dinh Nguyen, Oh‐Soon Shin, Nguyễn Minh Quân
article en

Abstract

The integration of unmanned aerial vehicles (UAVs) into wireless communication systems has emerged as a transformative approach, promising cost-efficient connectivity. This letter addresses the optimization of the dynamic time-splitting ratio and flight trajectory for a communication system linking a ground base station to the UAV equipped with backscatter devices (referred to as UB), and from UB to an end user. Given the inherent non-convexity of the problem, we develop two meta-heuristic-based approaches inspired by genetic algorithm and particle swarm optimization to enhance the total achievable rate while reducing computational complexity. Numerical results demonstrate the effectiveness of these meta-heuristic solutions, showcasing significant improvements in the achievable rate and computation time compared to existing benchmarks.

IEEE Communications LettersVol. 29(6)
Soongsil University (KR), VinUniversity (VN), Hanoi University of Science and Technology (VN)
Affordable and clean energy
Openalex Percentile: Top 92%
Satellite Communication Systems
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