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 Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用对抗训练增强毫米波 MIMO 无线通信系统的安全性

毫米波MIMO无线通信系统部署在5G和下一代网络中。深度学习模型对于提高这些系统性能的有效性已在文献中得到证明。然而，一些深度学习模型容易受到安全威胁，例如对抗性攻击。因此，对于这些系统的部署，必须使其能够抵御此类攻击，以实现高质量的安全通信。对抗性训练是一种预先训练深度学习模型以应对对抗性攻击的解决方案。本文实现了快速梯度符号法、迭代快速梯度符号法和动量迭代快速梯度符号法三种对抗攻击的对抗训练。模拟结果表明，经过对抗性训练后，即使在应用了对抗性攻击之后，接收端的错误也有所减少。© 2024 日本电气工程师协会。由 Wiley 期刊有限责任公司出版。