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Deep Learning for Secure UAV Swarm Communication Under Malicious Attacks
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DOI：10.1109/TWC.2024.3419923.png)
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摘要
En 中文
Unmanned aerial vehicle (UAV) swarms have become a promising solution to enhance modern wireless communication in complicated environments. However, due to the existence of real-world malicious attacks, the performance of prediction and optimisation methods used for UAV swarms are easily degraded. In this paper, we propose a novel deep learning-based user mobility prediction, user assignment and drone position optimisation scheme for a UAV swarm-enabled wireless communication system in the presence of malicious Global Navigation Satellite System (GNSS) spoofing attackers. Specifically, a robust deep learning-based user mobility prediction model, namely denoising autoencoder recurrent transformer (DART), is designed. Additionally, two efficient user assignment and drone position optimisation methods are proposed. The proposed deep learning model forecasts user locations, on which we construct and solve assignment and position optimisation problems. Simulation results show that the proposed deep learning-based prediction-optimisation scheme can provide up to 30% higher overall sum rate compared with the adversarially trained long short-term memory (LSTM) baseline and almost double the overall sum rate compared with the vanilla LSTM baseline.
Keyword:
Autonomous aerial vehicles
Global navigation satellite system
Drones
Predictive models
Trajectory
Deep learning
Prediction algorithms
Unmanned aerial vehicle swarm
malicious attack
deep learning
denoising autoencoder recurrent transformer (DART)
differential programming
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
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