Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks. (arXiv:2305.02741v1 [cs.CR])

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 …

5g networks applications augmented reality aware cars channel communication critical deep learning driving experience high networks quality reliability security self-driving self-driving cars systems uncertainty wireless