5G Channel Estimation using Deep Learning

Abstract

The abstract focuses on the integration of 5G channel estimation and the vulnerability of deep learning models, specifically in the context of OFDM signals, while employing a student-teacher model architecture. Channel estimation is a crucial aspect of 5G communication systems, ensuring reliable data transmission in dynamic wireless environments. Simultaneously, the advent of deep learning introduces susceptibility to adversarial attacks, where malicious inputs can deceive the model's predictions. This paper explores the intricate relationship between 5G channel estimation and deep learning vulnerabilities, emphasising the application of a student-teacher model to enhance system robustness. By delving into the nuances of OFDM signals, the study aims to provide a comprehensive understanding of how these elements intertwine, offering insights into potential security enhancements for next-generation wireless communication systems.