## 3 Channel Estimation in Different Frequency Bands
### Channel Estimation for RIS Systems in High Frequency Bands
#### 3.1.1 Channel Estimation for Fully-Passive RIS
**Practical application:** The impact of its hardware cost and accidents on RIS need to be considered. The increase in the number of antennas in large-scale MIMO systems leads to high power consumption and high cost. [PERSON] et al. [39] proposed to equip the BS with a small number of analog-to-digital converters (ADCs). The channel estimation was then completed using the bilinear generalized approximate message passing (BiG AMP) algorithm. The method in this paper requires only 8 bit quantization to achieve the same results as infinite bit. To address the information loss due to quantization, a Bayesian optimal estimation framework was introduced to reduce the cost and power consumption. RIS was proposed in [40] to apply low-precision ADC quantization in large-scale MIMO communication systems. Further, a channel estimation algorithm based on GAMP algorithm and combined with expectation maximization (EM) and nearest neighbor learning (NNL) algorithms was proposed. It compensated for the loss caused by low precision ADC quantization, which can guarantee the estimation performance while reducing power consumption. To solve array blocking problems caused by sand, dust, rain, and snow cover in practical applications, the joint array diagnosis and channel estimation methods applied to single-user and multi-users were proposed in [40], respectively. It reduced the influence caused by the outside world to improve channel estimation accuracy and usage efficiency. To eliminate inter-cell and intra-cell interference, [PERSON] et al. [41] achieved a proper phase shift for each element design by designing different reflection coefficients. It ensured that the performance of the desired user was maximised and that interference to the undesired user was minimised. An efficient 2D line spectrum optimization based on ANM was also proposed for channel estimation, and the design of reflection beamforming was accomplished using this method, but the performance was limited when the number of users increased. Integrating RIS into vehicles can achieve good applications in the future. The mobility of vehicles makes the CSI change contiguously, which makes it difficult to obtain accurate instantaneous CSI. Therefore, a transmission protocol with a reasonable configuration of CSI acquisition time scale was proposed in [42], which can reduce the channel information update frequency, the proposed method can reduce the complexity and training overhead effectively. **Wideband systems:** The proposed method for narrowband systems is not generally applicable in wideband systems. [PERSON] et al. [43] proposed to use OFDM to overcome frequency selective fading in wideband, and then used BS-RIS as prior knowledge to design the pilot to estimate BS-UE and RIS-UE by the distributed OMP (DOMP) method jointly. [PERSON] et al. [44] transformed the wideband channel estimation into a parameter recovery problem. Several pilot symbols were used to detect the channel parameters by a newtonian OMP (NOMP) algorithm. This method considered the phase and delay differences between the received signals at different BS antennas and RIS elements. [PERSON] et al. [45] applied the super-resolution (SR) method in images to channel estimation. Then, they proposed a switch-based method for LS estimation and super-resolution, which can reduce the training overhead while ensuring the channel estimation accuracy. [PERSON] et al. [46] proposed a channel estimation method based on canonical polyadic decomposition (CPD) by representing the received signal as a third-order tensor using the inherent sparsity of mmWave. Then, the channel parameters were estimated based on the structured CPD method. The problem of high pilot overhead due to a fully passive RIS can often also be solved by assisted methods. [PERSON] et al. [47] proposed an approach of using reference points for separate channel estimation to overcome the problem of large training overhead for cascaded channels. The proposed method achieves a 2 dB reduction in NMSE compared with the best benchmark solution.