## 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
Partial information of a single user can be estimated in the first. Further, the channel between BS and RIS is estimated in a large time scale using the quasi-static property between BS-RIS, and the channel estimation for multi-user is completed using channel recovery. Additionally, using the case that the physical locations of BS, RIS and UEs do not vary significantly over multiple consecutive channel coherence blocks, the remaining channel coherence blocks only need to estimate the rapidly varying channel gain. Compared with the baseline solution, only 50% of the pilot overhead is used to achieve better results. Moreover, a RIS-assisted mmWave channel estimation method based on the Re'nyi entropy function was proposed in [31]. The Re'nyi entropy function is utilized as a sparse promoting regularizer for the purpose of reducing the pilot overhead. When utilizing CS algorithms, the complexity is cube proportional to the number of RIS components. [PERSON] et al. [24] transform the channel estimation problem into a fixed constraint rank one parametric regularization optimization problem. The manifold optimisation (MO) and alternating minimisation (AM) methods are used to find the local optimal solution. To further reduce the complexity, a three-part CS-based channel estimation method was proposed [24]. Optimisation-based algorithms can improve accuracy, but they also introduce a greater degree of complexity. On the other hand, greedy algorithms can reduce the complexity relatively well, but also the accuracy. To improve the estimation performance and robustness, in sparse channel recovery, a predefined dictionary is used which leads to grid mismatch and performance degradation. [PERSON] et al. [32] proposed to optimize the dictionary to adapt to the channel characteristics. BS and RIS keep the position constant in most cases. [PERSON] et al. [33] estimated the angular information by using the position information to achieve the purpose of reducing the pilot overhead. The one-dimensional (1D) and two-dimensional (2D) ANM-based channel estimation method is further proposed to achieve excellent estimation accuracy. To improve the ability to apply different scenarios and reduce the pilot overhead, [PERSON] et al. [34] first estimated some of the active users and in the second stage estimated inactive users based on the spatial-temporal-spectral (STS) framework of deep convolutional neural networks (CNN). Deep learning networks can sometimes have a low generalisation ability and cannot be applied to changing scenarios. Taking it a step further, [PERSON] et al. [35] proposed a distributed machine learning (DML) technique to achieve reliable downlink cascaded channel estimation and neural network architectures. Deep learning is an effective way to improve the estimation performance. Three practical residual neural networks, namely, generative adversarial networks based convolutional blind denoising (GAN-CBD) network, convolutional blind denoising network (CBDNet), and multiple residual dense network (MRDNet), were proposed in [36] using the low-rank structure of the channel. Specifically, the GAN-CBD and CBDNet based on the generative adversarial network were used in the offline phase to obtain accurateCSI, and then the MRDNet was used to adapt the online cascaded channel estimation with improved generalization and fitting capabilities flexibly. A semi-blind joint channel estimation and symbol detection scheme was proposed in [37]. The method proves that the BS received signal follows the PARATUCK2 tensor model and there is no training phase, symbol detection, and channel estimation are done simultaneously. It is difficult to meet the demand for a large number of training symbols in practice, and two Cramer-Rao lower bound (CRB)-based training signal design methods for enhanced sparse channel estimation were proposed in [38]. The CRB for the channel parameters consisting of path gain and path angle is derived under the assumption of Bayesian mixture parameters.