With the rise of intelligent applications, such as self-driving cars and augmented reality, the security and reliability of wireless communication systems have become increasingly crucial. One of the most critical components of ensuring a high-quality experience is channel estimation, which is fundamental for efficient transmission and interference management in wireless networks. However, using deep neural networks (DNNs) in channel estimation raises security and trust concerns due to their complexity and the need for more transparency in decision-making. This paper proposes a Monte Carlo Dropout (MCDO)-based approach for secure and trustworthy channel estimation in 5G networks. Our approach combines the advantages of traditional and deep learning techniques by incorporating conventional pilot-based channel estimation as a prior in the deep learning model. Additionally, we use MCDO to obtain uncertainty-aware predictions, enhancing the model's security and trustworthiness. Our experiments demonstrate that our proposed approach outperforms traditional and deep learning-based approaches regarding security, trustworthiness, and performance in 5G scenarios.

翻译：随着自动驾驶汽车和增强现实等智能应用的兴起，无线通信系统的安全性和可靠性变得日益关键。确保高质量体验的最重要环节之一是信道估计，它是无线网络中高效传输和干扰管理的基础。然而，在信道估计中使用深度神经网络（DNN）因其复杂性和决策过程缺乏透明度而引发安全与信任问题。本文提出一种基于蒙特卡洛丢弃（MCDO）的方法，用于5G网络中安全可信的信道估计。我们的方法将传统导频辅助信道估计作为深度学习模型的先验知识，从而融合了传统技术与深度学习技术的优势。此外，我们利用MCDO获取具有不确定性感知的预测结果，增强了模型的安全性和可信度。实验表明，在5G场景下，本文所提方法在安全性、可信度和性能方面均优于传统方法和基于深度学习的方法。