Security Enhancement of mmWave MIMO Wireless Communication System Using Adversarial Training

Millimeter wave MIMO wireless communication systems are deployed in 5G and next‐generation networks. The effectiveness of deep learning models for improving the performance of these systems has been proven in the literature. However, several deep learning models are vulnerable to security threats, s...

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Bibliographic Details
Published in:IEEJ transactions on electrical and electronic engineering 2024-06, Vol.19 (6), p.967-974
Main Authors: Saini, Mehak, Grewal, Surender K.
Format: Article
Language:English
Subjects:
adversarial training
Deep learning
FGSM
I‐FGSM
Millimeter waves
MIMO
MIMO communication
MI‐FGSM
mmWave
Security
System effectiveness
Wireless communication systems
Wireless communications
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Summary:Millimeter wave MIMO wireless communication systems are deployed in 5G and next‐generation networks. The effectiveness of deep learning models for improving the performance of these systems has been proven in the literature. However, several deep learning models are vulnerable to security threats, such as adversarial attacks. Therefore, for the deployment of these systems, it is essential to make them resilient to such kinds of attacks for good quality secure communication. Adversarial training is a solution by which deep learning models are trained for adversarial attacks beforehand. Adversarial training for three types of adversarial attacks, that is, Fast Gradient Sign Method, Iterative Fast Gradient Sign Method, and Momentum Iterative Fast Gradient Sign Method is implemented in this paper. The simulation results depict a decrease in the error at the receiving end after adversarial training, even after an adversarial attack has been applied. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
ISSN:1931-4973
1931-4981
DOI:10.1002/tee.24025