## 2 Objectives and Contributions  
With RIS gradually gaining attention, a number of reviews on RIS channel estimation have appeared in recent years. [PERSON] et al. (2020) provided an overview of RIS-assisted wireless communication systems, describing the principles, hardware architecture, and applications relating to RIS. [PERSON] et al. (2020) focused on promising research directions and emerging RIS architectures. [PERSON] et al. (2020) and [PERSON] et al. (2020) outlined channel estimation schemes for RIS-assisted communication systems and channel modeling. [PERSON] et al. (2020) presented RIS-aided wireless communications and RIS-based information transmission when the RIS acts as transmitter and reflector, respectively. [PERSON] et al. (2020) and [PERSON] et al. (2020) reviewed the relevant knowledge about RIS in the high frequency band. [PERSON] et al. (2020) mainly summarized the structured and unstructured system models of RIS. The authors analyze and balance the two systems through several metrics. Finally, the authors briefly add the study of algorithms such as deep learning.  
Table 2 describes the ten public overviews for RIS-assisted wireless communication systems, presenting the main direction and major contribution for each overview.  
In this paper, we provide an overview of channel estimation methods for RIS-assisted communication systems in terms of both different frequency bands and different algorithms. Specifically, we focus on the RIS-assisted channel estimation models in different frequency bands. We have focused on comparing the differences in channel estimation methods in different frequency bands. We found representative works and performed a quantitative analysis. In the following classification of the different algorithms, we focus on techniques related to deep learning RIS channel estimation algorithms, such as building end-to-end networks, introducing image processing algorithms and fusing conventional schemes with deep learning. This paper focuses on summarizing the different RIS channel estimation schemes and signal processing algorithms in different frequency bands, considering a comprehensive overview of the existing literature on channel estimation schemes.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Year** & **Main Direction** & **Major Contribution** \\ \hline [PERSON] et al. (2020) & 2021 & Reflection optimization and channel estimation for RIS-assisted mmWave/sub-terahertz (THz) communication channel estimation and passive beamforming design & Reflection channel models, practical constraint, and hardware architecture Technical challenges, channel estimation frameworks, and training signal design \\ [PERSON] et al. (2020) & 2022 & Channel estimation and passive beamforming design & Discussed emerging RIS architectures, applications, and practical design problems Channel estimation, transmission design, radio localization, etc. \\ [PERSON] et al. (2020) & 2022 & Channel estimation & Wireless communication standards, the current and future standardization activities Reflection principle, channel estimation, system designs, etc. \\ [PERSON] et al. (2020) & 2021 & Channel estimation and system design system designs, etc. & Channel modeling, new material exploration, etc. \\ [PERSON] et al. (2020) & 2021 & Hardware design, channel estimation, etc. & Summarize the structured and unstructured system models in RIS systems Phase-shift optimization and resource allocation Main recent techniques and various strategies \\ [PERSON] et al. (2020) & 2022 & Channel estimation & \\ [PERSON] et al. (2020) & 2022 & Channel estimation & \\ [PERSON] et al. (2021) & 2022 & Channel estimation & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Representative overview/survey papers on RIS.