###### Abstract
The primary purpose of using deep learning for channel estimation is to enhance the receiver's performance, thereby optimizing the recovery process for impaired transmitted data. Compared to traditional methods, deep-learning-based methods have greater advantages in terms of estimation accuracy or computation overhead in complex channel environments. After thoroughly reviewing the relevant literature, we observe that deep-learning-based channel estimation methods can fall into two distinct classes: data-driven and model-driven. The data-driven approach abandons the modular structure of wireless communication systems and replaces it with neural networks trained on massive data. In 2017, the authors of [9] started to utilize a data-driven deep learning approach for implicit channel estimation. In this procedure, the orthogonal frequency division multiplexing (OFDM) receiver is regarded as a \"black box\", and the channel estimation and signal detection modules are substituted by a deep neural network (DNN). The DNN is trained offline to obtain the best parameters and then deployed online to directly recover the transmitted data. For massive multiple-input-multiple-output (MIMO) systems, the data-driven approach proposed in [10] employs a multilayer perceptron (MLP). This method does not leverage channel statistics and can achieve considerable performance gains over traditional channel estimators in low signal-to-noise ratio (SNR) regions. The model-driven approach is based on known physical layer prior knowledge, decreasing the need for data [11]. The ComNet developed in [12] is a typical example of model-driven schemes. Unlike the data-driven method in [9], ComNet retains the basic OFDM system framework. In ComNet's channel estimation module, a DNN learns the error between the least square (LS) estimation and the actual channel response, resulting in a more accurate channel estimation. In ComNet's signal detection module, the network with nonlinear activation functions introduces nonlinearity, making the module a nonlinear signal detector that can more effectively process and recognize nonlinear features and distortions within the signal. In the model-driven scheme proposed in [13] for millimeter-wave massive MIMO systems, neural networks are fused into an iterative signal reconstruction algorithm to estimate channels. This scheme improves the dilemma of difficult CSI acquisition in scenarios where antenna arrays are dense, and receivers are equipped with restricted radio frequency (RF) chains. Numerous studies have shown that deep-learning-based methods can outperform traditional methods, and they hold the potential to perform well in future wireless communications. In recent years, some scholars [14; 15; 16; 17; 18] have reviewed the research on channel estimation based on deep learning. The main contributions of these reviews are summarized in Table 1. While these previous reviews offer valuable assistance on deep-learning-based channel estimation, their discussion is limited to traditional communication scenarios. However, it is crucial to recognize that the emergence of techniques such as the reconfigurable intelligent surface (RIS) [19] has spawned novel communication scenarios that present novel challenges and opportunities. Therefore, to better assist future research, it is also necessary to review state-of-the-art deep-learning-based methods for emerging scenarios, such as scenarios incorporating the RIS technique. RIS, also referred to as intelligent reflection surface (IRS), is a promising 6G communication technique. It can achieve multiple purposes, including capacity expansion, coverage expansion, interference suppression, high beamforming gain, and improving the wireless propagation environment [20]. Nevertheless, RIS is an approximately passive device that cannot actively transmit and receive signals or further process them. This limitation, coupled with the introduction of cascaded channels with high dimensionality, presents a challenge in channel estimation.