Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3112392Utgivelsesdato
2023Metadata
Vis full innførselSamlinger
- Institutt for elkraftteknikk [2500]
- Publikasjoner fra CRIStin - NTNU [38672]
Originalversjon
10.1109/MECO58584.2023.10155011Sammendrag
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 wire-less 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 outper-forms traditional and deep learning-based approaches regarding security, trustworthiness, and performance in SG scenarios.