A review on Recent Trends in Deep Learning-based Channel Estimation Techniques

Accurate channel estimation is essential for improving the performance and reliability of data transmission in wireless communication systems. Deep learning techniques have become a promising approach for channel estimation due to their ability to learn complex relationships in data and improve accuracy compared to traditional methods. In recent years, there have been significant developments in deep learning-based channel estimation techniques, with a focus on improving performance in complex environments, reducing computational complexity, and enhancing robustness to channel impairments. This survey paper provides a detailed review of the latest advances in deep learning-based channel estimation techniques. It covers the basic principles of deep learning and its application in wireless communications, discusses the most relevant deep learning architectures and algorithms for channel estimation, and explores the latest research trends in this area. The paper also examines the challenges and limitations of deep learning-based channel estimation.