## 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
**Ref.** & **Year** & **System Setup** & **Problem** & **Method** & **Results Analysis** \\ \hline [PERSON] et al. [31] & 2022 & MIMO & High pilot overhead & Extends the Re\({}^{\prime}\)nyi entropy function as the sparsity-promoting regularizer & An improvement over the OMP \\ [PERSON] et al. [24] & 2022 & MIMO & Inefficient method & A MO and AM based method and a three-stage algorithm & The MO method had good results when the overhead was higher than 130. The CS algorithm & The MO method had good results when the overhead was higher than 130. The CS method proposed in this paper is better than GAMP \\ [PERSON] et al. [32] & 2022 & MISO & Grid mismatch issues and estimation performance degradation & Dictionary is optimized to adapt to channel characteristics & Better than the predefined dictionary method when the training overhead was small \\ [PERSON] et al. [35] & 2022 & MIMO & High training overhead & The DML algorithm & Used in different scenarios \\ [PERSON] et al. [36] & 2022 & MIMO & Low estimation accuracy & GAN-CBD, CBDNet, and MRDNet & MRDNet achieved better MMSE performance than GAN-CBD and CBDNet, with improvements of 5.63 dB and 4.51 dB, respectively \\ [PERSON] et al. [37] & 2022 & MIMO & Low estimation accuracy & Semi-blind joint channel estimation and symbol detection algorithm & Better NMSE and BER performance \\ [PERSON] et al. [38] & 2022 & MIMO & Fewer training symbols & Two CRB-based training signal design algorithms for enhanced sparse channel estimation & Significant performance gain when the number of training symbols was less than the number of RIS reflection elements \\ [PERSON] et al. [41] & 2022 & SISO & Interference problems & Maximize power at desired users and eliminate interference at undesired users & The reflector element changed from 8 to 16, achieved a power gain of approximately 10 dB \\ [PERSON] et al. [42] & 2022 & MIMO & High mobility leads to CSI changes and requires high overhead & A reasonable configuration of the CSI acquisition time scale & The communication performance was improved in mobile vehicle scenarios \\ [PERSON] et al. [46] & 2022 & MIMO & High training overhead & The received signal is represented as a low-rank third-order tensor & Significantly reduced training overhead and better performance compared with SOMP algorithm \\ [PERSON] et al. [47] & 2022 & MIMO & High training overhead & Used reference points to aid estimation & NMSE was reduced by 2 dB compared with the best benchmark solution (\(r_{\text{SNR}}=10\) dB) \\ [PERSON] et al. [22] & 2023 & MIMO & High training overhead & Two-stage channel estimation method using common sparse structure & Significantly reduced and better respectively (\(e_{\text{NMSE}}=10^{-2}\)) \\ [PERSON] et al. [33] & 2023 & MIMO & High training overhead & Location-aware channel estimation based on ANM & NMSE was reduced by 2 dB compared with the best benchmark solution (\(r_{\text{SNR}}=10\) dB) \\ [PERSON] et al. [22] & 2023 & MIMO & High training overhead & Two-stage channel estimation method using common sparse structure & Significantly reduced and better performance compared with NMSE was reduced by 2 dB compared with the best benchmark solution (\(r_{\text{SNR}}=10^{-2}\) dB) \\ [PERSON] et al. [33] & 2023 & MIMO & High training overhead & Location-aware channel estimation based on ANM & NMSE was reduced by 2 dB compared with the best benchmark solution (\(r_{\text{SNR}}=10\) dB) \\ [PERSON] et al. [22] & 2023 & MIMO & High training overhead & Two-stage channel estimation method using common sparse structure & The MO method had good results when the overhead was higher than 130. The CS algorithm proposed in this paper is better than GAMP \\ [PERSON] et al. [39] & 2023 & MIMO & High cost & Bayesian optimal estimation framework & The BiG AMP algorithm had better performance in few bit quantization, and 8 bit quantization was almost as good as infinite bit quantization \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._