## 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  
The fully passive RIS can perform cascaded channel estimation due to the inability to transmit and receive signals. Due to the inability to decouple well in high-dimensional channels and the scaling ambiguity, it is difficult to obtain CSI for BS-RIS and RIS-UE. Therefore, several active RIS elements are proposed to be placed in the RIS, as shown in Figure 2b, which can both exploit the advantages of RIS in saving power to a certain extent and accomplish signal processing more efficiently. An architecture with only a few active elements was proposed in [48]. A CS method was proposed to perform channel reconstruction for sampled channels sensed by a few elements. Another method was to use deep learning to learn the best mapping from the best reflection matrix. [PERSON] et al. [49] proposed a receiver element RIS structure equipped with only a portion of 1-bit quantization. The training signal at the passive RIS element was first recovered using the alternating direction method of multipliers (ADMM), and then the full channel information was obtained using channel sparsity and the GAMP algorithm. A deep denoising neural network-assisted compressed channel estimation method was proposed in [50]. The complete channel matrix was obtained from partial cells using the channel reconstruction method. The use of denoising networks can achieve performance gains of up to 4 dB above the initial estimation. [PERSON] et al. [51] proposed an approach to reshape the channel matrix into a two-dimensional image, using single-scale enhanced deep super-resolution (EDSR) neural network and multi-scale deep super-resolution (MDSR) neural network to recover the channel using low sparse channel properties. This approach can increase the generalization capability and reduce the hardware complexity. [PERSON] et al. [52] considered time-varying channels and proposed a hybrid RIS structure composed of active and passive elements. The narrowband was modeled as a third-order CPD problem and the wideband problem as a fourth-order CPD problem. Then, the tensor problem was solved by an algebraic method to recover the channel parameters and complete the channel estimation. This can help to improve the accuracy of estimating and reduce the complexity of estimating. In the high frequency band, the channel estimation problem is mostly converted to a compressed sensing problem because of its channel sparsity characteristics. It can be solved using greedy algorithms, message passing algorithms, and optimisation algorithms. Using this feature and the channel estimation protocol, up to 80% reduction in pilot overhead can be achieved in the method proposed in this paper. In practical situations, the hardware losses and costs associated with high frequency bands need to be considered. It is also necessary to consider the Doppler shift caused by the actual movement of cars, high-speed trains, etc. Therefore, a suitable channel estimation method needs to be selected. The complexity can be reduced by using deep learning methods instead of algorithms, and of course also for denoising purposes. The channel estimation algorithms, and performance comparison in high frequency bands are described in Table 3. Table 3 describes the methods from four aspects: system setup, problem, method, and results analysis.