###### Abstract  
With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed. channel estimation; deep learning; wireless communication; physical layer; intelligent communication; RIS 2023124965122449651224496  
transmitted and received signals heavily relies on channel state information (CSI), and the goal of channel estimation is to estimate CSI at the receiver. Accurate channel estimation helps in compensating for the amplitude and phase of the received signal, facilitating the precise recovery of the original data when demodulated at the receiver. For these reasons, channel estimation is a key part of physical layer wireless communication. In light of this, enhancing its performance can significantly improve the quality of communication networks, help overcome the performance limitations of 5G, and pave the way for achieving the \"Intelligent Interconnection of Everything\" goal of 6G [4; 5]. Conventional channel estimation methods are typically classified as pilot-based, blind, and semi-blind. Due to the simplicity of the principle behind the pilot-based channel estimation method, it can be implemented relatively quickly in practice. However, this method requires transmitting both data and pilot symbols, which leads to low spectrum utilization and reduced transmission efficiency. While the blind channel estimation method does not require pilots, it is computationally complex, sluggish to converge, and demands many statistical samples. The semi-blind channel estimation method represents a compromise approach that integrates the benefits of pilot-based and blind channel estimation methods. Nevertheless, it is worth noting that the implementation complexity of this method remains substantial. It is evident that, with their significant limitations, conventional channel estimation methods are inadequate for addressing the growing complexity of future communication systems. Hence, there is a pressing need to develop a novel approach that exhibits enhanced precision, efficacy, and intelligence in estimating CSI. The ongoing advancement of artificial intelligence in recent years has sparked a fresh surge of technological innovation in human society. As a significant research avenue in artificial intelligence, deep learning has successfully addressed a range of previously challenging issues. It has exhibited excellent performance across numerous areas, including speech recognition, natural language processing, and computer vision [6]. In wireless communications, applying deep learning to the physical layer has become a hot research topic, and papers [7; 8] summarize the possible research directions. In this paper, the focus is on exploring the application of deep learning to channel estimation.