## 4 Channel Estimation Based on Deep Learning for RIS-Aided Communication Systems
### Application of Deep Learning in Channel Estimation for RIS-Aided MU Systems
To meet the requirements of the real-world deployment of RIS, the authors further designed the lightweight DCSaNet-l network, which reduces the parameters by a significant amount (nearly 83.7%) but performs almost as well as DCSaNet. The simulation results demonstrate that these networks substantially improve the NMSE performance and the training speed across various channel dimensions and SNR levels. This section focuses on deep-learning-based channel estimation methods for RIS-aided communication systems. These methods cover massive MIMO, mmWave, and MU systems and are summarized in Table 6. With deep learning, we can more accurately estimate complex RIS channels. However, there may still be challenges in this area of research. First, most existing studies on RIS focus on passive RIS. The passive reflection elements of RIS introduce high-dimensional channel parameters, which increase the input dimensions of the deep learning models. As a result, the complexity and training difficulty of the models rise. Second, the deployment location of RIS and the surrounding environment impact its reflective properties. Hence, the model must have a strong generalization capability, ensuring its performance in various scenarios is as effective as the one it was trained for. Given these, how to combine RIS technology with deep learning technology more effectively is an important topic worth studying in the future. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Reference** & **System** & **RIS Architecture** & **Deep Learning Model** & **Input** & **Output** & **Complexity** \\ \hline [PERSON] et al. [80] & RS-aided massive MIMO & Entirely passive & ResNet & OMF rough channel & Accurate channel & \(O(k\text{MIMO}_{+}+\sum_{j=1}^{D}M_{ij})\) \\  & & & estimation result & estimation result & estimation result & \(C_{1-}(C_{1})\) \\ \hline [PERSON] et al. [58] & RS-aided massive MIMO & Entirely passive & ResU-Net & Recovered pilot signal & Estimated channel & CPU time = 95.7 ms, GPU time = 4.27 ms \\ \hline [PERSON] et al. [60] & Double-RIS-aided massive MIMO & Entirely passive & SC-attention network & LS rough channel & Accurate channel & \\  & & & estimation result & estimation result & \(\backslash\) \\ \hline [PERSON] et al. [88] & RS-aided mmWave MIMO & Entirely passive & STS-CNN & Partial CSI & Entire CSI & \(\backslash\) \\ \hline [PERSON] et al. [89] & RS-aided mmWave & Semi-passive & EDR + MDSR & Partial CSI & Entire CSI & \(\backslash\) \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline [PERSON] et al. [95] & RS-aided mmWave MIMO & Entirely passive & DCNN & Recovered pilot signal & Residual noise & \(\frac{O(\text{IN}_{\text{LS}}+UL_{\text{LS}})}{+UL_{\text{LS}}(20^{2}+L_{ \text{LS}})}\) \\  & & & & & & & \\ \hline [PERSON] et al. [92] & RS-aided MU (SIMO-OFDM & Entirely passive & DNN & LS rough channel & Accurate channel & \\  & & & estimation result result & estimation result & estimation result & \\ \hline [PERSON] et al. [95] & RS-aided MUC & Entirely passive & CDNN (CNN + DEN) & Noisy channel matrix & Drop induced channel matrix & computation time = 2.66 ms \\ \hline [PERSON] et al. [94] & RS-aided MU & Entirely passive & SRDNet & Estimated channel at pilot positions & Estimated whole channel & Predict time = \(1.61\times 10^{-2}\) s \\  & & & & & & \\  & & & & & & \\  & & & & & \\ \hline [PERSON] et al. [95] & RS-aided MUC & Entirely passive & DCNNNet (dilated) & LS rough channel & Accurate channel & Execution time = \(5.2\times 10^{-4}\) s, resolution time = \(5.2\times 10^{-4}\) s \\  & & & & estimation result & estimation result & training time = 2.66 s \\ \hline \end{tabular}
\end{table} 
Table 6: Research on channel estimation based on deep learning for RS-aided communication systems.