###### Abstract
As a result, conventional channel estimation methods are computationally expensive and inaccurate for practical RIS-aided communication systems. In this context, deep learning is particularly apt for addressing channel estimation challenges in RIS-aided communication systems, owing to its ability to effectively solve nonlinear mapping problems. Specifically, this approach achieves its goal by learning an approximate mapping function from the training data to the CSI of a single or cascaded channel. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article** & **Year** & **Main Contribution** \\ \hline
[14] & 2019 & It groups the challenges (such as feedback overhead) in massive MIMO channel modeling and estimation according to four technical details, followed by a discussion of each group’s relevant deep-learning-based solutions. \\ \hline
[15] & 2020 & It outlines how to use deep learning to enhance the performance of massive MIMO channel estimation while reducing training overhead, followed by introducing some data-driven methods. \\ \hline
[16] & 2022 & It reviews various deep learning models used for channel estimation. Subsequently, it introduces the channel estimation methods that use deep learning in different systems. \\ \hline
[17] & 2022 & It overviews recent channel estimation methods using deep learning in doubly-dispersive channels and conducts experimental comparisons and simulation analyses under various frame sizes, modulation orders, and mobility scenarios. \\ \hline
[18] & 2022 & It provides a comprehensive summary of artificial intelligence-assisted channel estimation methods in multicarrier systems, which include machine learning and neural networks. \\ \hline This review & 2023 & This paper discusses data-driven channel estimation methods based on different fundamental neural networks. It also introduces model-driven methods, which are categorized according to various benchmark algorithms. Additionally, this paper overviews recent studies on deep-learning-based channel estimation in RIS-aided communication systems. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of recent review papers on deep-learning-based channel estimation. Given the ongoing advancements in wireless communication technologies and the vital role played by deep learning in solving channel estimation problems, we provide a review of deep-learning-based channel estimation. The main contributions of this paper are as follows:  
* Before discussing deep-learning-based channel estimation methods, conventional methods are introduced and classified as pilot-based, blind, and semi-blind, followed by an analysis of the advantages and disadvantages of each category. * Dividing deep-learning-based channel estimation methods into data-driven and model-driven approaches, then reviewing their recent studies based on neural network types and benchmark algorithm types, respectively. * We extend the discussion on applying deep learning to channel estimation to RIS-aided communication systems, which are emerging scenarios in next-generation wireless communications. The remainder of this paper is structured as follows: Section 2 introduces the three main classes of conventional channel estimation methods and analyzes their advantages and disadvantages; Section 3 presents several commonly used neural networks and reviews recent research on deep-learning-based channel estimation; Section 4 discusses the application of deep learning in channel estimation for RIS-aided communication systems; Section 5 describes the challenges and future research trends of deep-learning-based channel estimation; and finally, Section 6 summarizes the entire paper. Figure 1 illustrates the organization of this paper.