English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks