## 3 Channel Estimation in Different Frequency Bands
### Channel Estimation for RIS Systems in High Frequency Bands
#### 3.1.2 Channel Estimation for Semi-Passive RIS
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Year** & **System Setup** & **Problem** & **Method** & **Results Analysis** \\ \hline [PERSON] et al. [26] & 2020 & MISO & High training overhead & Conversion to sparse channel recovery problem & Approximate NMSE of 0.04 obtained using the GAMP algorithm & Approximate NMSE of 0.04 \\ [PERSON] et al. [43] & 2020 & MIMO & Wideband system estimation issues & DOMP algorithm and redundant dictionary & GAMP algorithm \\ [PERSON] et al. [50] & 2020 & MIMO & High training overhead & The deep denoising neural network assisted & 4 dB performance gain over initial estimation \\ [PERSON] et al. [34] & 2021 & MIMO & High training overhead & Estimate active users and use CNN’s STS framework to estimate inactive users & The larger the number of active users, the smaller the NMSE \\ [PERSON] [40] & 2021 & MIMO & High cost/array block & An EM-NNL-GAMP method/Joint array diagnosis and channel estimation algorithm & 4 dB performance gain over initial estimation \\ [PERSON] et al. [44] & 2021 & MIMO & High pilot overhead & Convert a parameter recovery problem and use NOMP algorithm & Low SNR and small number of pilots had better performance than OMP 10\% rate improvement compared with LS (\(r_{\text{SNR}}=35\) dB) \\ [PERSON] et al. [45] & 2021 & SISO & High training overhead & SR method in images & Better than baseline at high SNR \\ [PERSON] et al. [48] & 2021 & MIMO & High training overhead & Reshape the channel matrix into a two-dimensional image & As the proportion of active cells increases, EDSR had better NMSE performance and MDSR reduced complexity \\ [PERSON] et al. [49] & 2021 & MIMO & High hardware cost and energy consumption & Modeling as a CPD problem and solving tensor problems with algebraic algorithm & Reduced complexity and great results at high SNR \\ [PERSON] et al. [51] & 2021 & MIMO & Low estimation overhead & Reshape the channel matrix into a two-dimensional image & As the proportion of active cells increases, EDSR had better NMSE performance and MDSR reduced complexity \\ [PERSON] et al. [52] & 2021 & MIMO & The problem of time-varying channels & Modeling as a CPD problem and solving tensor problems with algebraic algorithm & Reduced complexity and great results at high SNR \\ [PERSON] et al. [27] & 2022 & MISO & High training overhead & Two-stage beam training and FALS & FALS required 45\% fewer training symbols compared with the OMP and ANM algorithms (\(e_{\text{NMSE}}=0.1\)) \\ [PERSON] et al. [28] & 2022 & MISO & High training overhead & Deep DNN assisted compressed channel estimation algorithm & NMSE decreased with increasing spatial and temporal sampling \\ [PERSON] et al. [29] & 2022 & SIMO & High training overhead & The model-driven deep unfolding neural network & Achieved the same NMSE with 25\% less training overhead than LS 60\% reduction in pilot overhead compared with baseline scheme (\(e_{\text{NMSE}}=10^{-3}\)) \\ [PERSON] et al. [23] & 2022 & MIMO & High training overhead & A three-stage estimation protocol using the correlation between typical users and normal users & 50\% reduction in pilot overhead compared with OMP (\(e_{\text{NMSE}}=10^{-2}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Channel estimation in high frequency bands sorted by publication years.