  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.
