A Review: Deep Learning Aided Channel Estimation Techniques for Wireless Communication System

Channel estimation is essential to wireless network system performance. Deep Learning (DL) has also shown considerable advances in improving communication reliability and lowering computing complexity in 5G networks. In spite of this, least square estimation is frequently used to provide channel estimates due to its small cost. The LS approach has a high level of estimation error due to the complexity with which Minimum Mean Square Error adjusts for noise to obtain higher performance than LS. Deep learning has demonstrated its ability to lower computational complexity while simultaneously enhancing system performance in 5G and future networks. This review study's focus is deep learning-aided channel estimation. In this study, channel estimation methods employing traditional and deep learning techniques are examined, and comparisons of various estimation methods are given. From the comparison study, we know the optimal deep learning types for Channel Estimation and the past or current approaches utilized for it.