Channel Estimation in 5G and Beyond Networks Using Deep Learning

Abstract

Channel estimation is a critical task in wireless communication for optimizing system performance and ensuring reliable communication. However, in 5G and beyond wireless communication systems, traditional channel estimation techniques are falling behind when it comes to handling large volumes of complex data of massive numbers of users being transmitted in dynamic and non-linear channel conditions. In response to this, a deep learning based channel estimation model that leverages the technique of image processing is studied in this work to perform channel estimation with very high accuracy. This work utilizes a deep learning model which is based on a Convolutional Neural Network trained on a custom generated 5G dataset allowing it to learn and recognize patterns of the Single Input Single Output channel. The results produced by the deep learning model outperform the traditional channel estimation techniques like Linear Interpolation and MATLAB's Practical channel estimation. The findings emphasize the potential of deep learning to revolutionize channel estimation techniques in 5G and Beyond Communication Systems and improve achieve massive connectivity efficiently. © 2024 IEEE.