## 5 Conclusions  
### Overall Discussion  
RIS, as a fundamental and innovative technology, has great potential and development prospects in future 6G. In this paper, we investigate the key technology of channel estimation for the emerging of RIS. The focus is on investigating the channel estimation methods for RIS-assisted wireless communication systems in high and low frequency bands applications and the comparison under different channel estimation algorithms. Specifically, we first compare the RIS channel estimation methods in different frequency bands, and analyze the differences and development directions of channel estimation methods in fully-passive RIS and semi-passive RIS. We introduce a system model using angular domain modeling to accomplish channel estimation, in which fewer parameters can save training overhead.  
We analyze the channel estimation methods in real situations, wideband, and different system settings. Completing channel estimation in the real case requires consideration of cost, fault detection, and mobility issues. Issues such as frequency selective fading need to be considered in wideband, and issues such as mutual interference and pilot pollution need to be considered in different system settings. Therefore, different channel estimation methods need to be used in different scenarios.  
We classify the channel estimation methods of RIS into conventional-based algorithms, optimization-based algorithms, CS-based algorithms, deep learning-based algorithms, and composition-based algorithms, and investigate the application of channel estimation in RIS with different algorithms. We analyze the advantages and disadvantages of different  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Year** & \begin{tabular}{c} **Antenna/RIS** \\ **Architecture** \\ \end{tabular} & **Major Problem** & \begin{tabular}{c} **Performance Analysis of Deep Learning** \\ **Model** \\ \end{tabular} \\ \hline [PERSON] et al. [45] & 2021 & SISO/Passive RIS & High complexity of conventional algorithms & \begin{tabular}{c} Proposed a high resolution network with \\ low-precision by linear interpolation to \\ achieve 92\% accuracy rate (\(r_{\text{SNR}}=35\) dB) \\ \end{tabular} \\ [PERSON] et al. [69] & 2022 & MIMO/Passive RIS & \begin{tabular}{c} Insufficient estimation performance \\ of the CS algorithm \\ \end{tabular} & \begin{tabular}{c} Proposed residual network to improve the \\ performance to outperform the OMP \\ (\(r_{\text{SNR}}=35\) dB) \\ \end{tabular} \\ [PERSON] et al. [113] & 2022 & MIMO/Passive RIS & \begin{tabular}{c} Insufficient performance of AMP \\ algorithm estimation \\ \end{tabular} & \begin{tabular}{c} Proposed a hypernetwork-assisted LAMP \\ network with dynamic shrinkage \\ parameters to reduce memory overhead by \\ 50\% and execution time by 93\% \\ \end{tabular} \\ [PERSON] et al. [112] & 2022 & SISO/Passive RIS & \
\begin{tabular}{c} Insufficient performance of \\ conventional algorithm estimation \\ \end{tabular} &
\begin{tabular}{c} Designed an end to end deep learning model \\ to reduce channel estimation overhead \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 9: Composition-based algorithms comparison for RIS channel estimation.  
methods, use the advantages and improve the disadvantages of different channel methods, and continuously search for more efficient channel estimation methods. We focus on emerging applications of deep learning in the field of RIS channel estimation, such as fusing deep learning with conventional solutions, combining the field of deep learning image processing with channel estimation and building end to end networks.