Energy-Efficient UAV Covert Communications via Metaheuristic Optimization
Abstract
Wireless communication systems increasingly require not only confidentiality but also transmission undetectability, motivating covert communications. This paper studies an unmanned aerial vehicle (UAV) enabled covert communication system where a UAV serves a legitimate receiver in the presence of multiple active and colluding wardens with imperfect location information. We maximize covert energy efficiency (CEE) by jointly optimizing the UAV's 3D trajectory and transmit power while accounting for energy. To address the resulting non-convex problem with robust covertness constraints, we develop Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)-based metaheuristic algorithms and compare them with a Block Coordinate Descent with Successive Convex Approximation (BCD-SCA) benchmark. Numerical results show that the proposed methods improve CEE while satisfying covertness constraints.
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BibTeX
@misc{quan2026energyefficient,
title = {{Energy-Efficient UAV Covert Communications via Metaheuristic Optimization}},
author = {Nguyen Minh Quan and Tran Thanh Thu and Nguyen The Ngoc Khue and Ninh Thi Thanh Tam and Trinh Van Chien},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00090},
eprint = {2611.00090},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00090},
}
RIS
TY - UNPB
TI - Energy-Efficient UAV Covert Communications via Metaheuristic Optimization
AU - Nguyen Minh Quan
AU - Tran Thanh Thu
AU - Nguyen The Ngoc Khue
AU - Ninh Thi Thanh Tam
AU - Trinh Van Chien
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00090
UR - https://arxiv.org/abs/2611.00090
AB - Wireless communication systems increasingly require not only confidentiality but also transmission undetectability, motivating covert communications. This paper studies an unmanned aerial vehicle (UAV) enabled covert communication system where a UAV serves a legitimate receiver in the presence of multiple active and colluding wardens with imperfect location information. We maximize covert energy efficiency (CEE) by jointly optimizing the UAV's 3D trajectory and transmit power while accounting for energy. To address the resulting non-convex problem with robust covertness constraints, we develop Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)-based metaheuristic algorithms and compare them with a Block Coordinate Descent with Successive Convex Approximation (BCD-SCA) benchmark. Numerical results show that the proposed methods improve CEE while satisfying covertness constraints.
ER -