## 4 Channel Estimation Based on Deep Learning for RIS-Aided Communication Systems
### Application of Deep Learning in Channel Estimation for RIS-Aided mmWave Systems  
Many researchers have pushed for the application of mmWave to enhance wireless system performance, which can generate narrow, highly energy-concentrated information-carrying beams. As one of the critical 6G techniques, mmWave offers higher transmission rates and greater connection capacity for wireless communications due to its ultra-high-frequency bandwidth. However, mmWave communication encounters challenges in coverage and signal quality. In addressing these issues, the emergence of RIS offers a promising solution: through the precise control of signals and improvement of their transmission properties, more excellent coverage can be achieved; additionally, by suppressing multipath fading and interference, the signal quality can be significantly enhanced [87]. Nevertheless, introducing RIS makes the higher-dimensional CSI need to be estimated. As an efficient tool, deep learning holds promise for successfully coping with this high-dimensional complexity. For RIS-aided channels, increasing users and reflective elements elevate the training overhead, making accurate CSI acquisition difficult. To deal with this issue, [PERSON] et al. [88] proposed a two-stage channel estimation scheme for entirely passive RIS-aided mmWave systems. In the initial stage, a pilot-based method for uplink is designed, where only randomly selected user terminals send known pilot sequences to the BS through the RIS, reducing the active user count during the training period. In the subsequent stage, a CNN-based spatial-temporal-spectral architecture (STS-CNN) is proposed to recover the CSI of inactive user terminals in the first stage. Through experimental comparison, this scheme outperforms the benchmark in terms of MSE. However, more research is needed on the CNN-based estimation method, as its complexity can be further reduced. For semi-passive RIS-aided mmWave systems with a few distributed active sensing devices, [PERSON] et al. [89] leveraged the sparsity of RIS-aided channels and used the image super-resolution method for channel estimation. In particular, their scheme used the multi-scale deep super-resolution (MDSR) and the enhanced deep super-resolution (EDSR) networks. First, the EDSR combines global feature fusion and residual blocks to obtain CSI for single-scale sparse channels, simplifying the RIS hardware. Next, the MDSR is employed to guarantee that the scale of active elements is adjustable to flexibly adapt to multi-scale, low-sparse, and low-resolution channel scenarios. Experiments demonstrate the scheme's effectiveness, and the estimation performance improves as the sensing device density and network size increase. In 2023, [PERSON] et al. [90] similarly transformed the channel estimation into an image super-resolution problem. Specifically, they developed a cascade channel estimation scheme for RIS-aided mmWave single-input-multi-output (SIMO) systems based on global attention ResNet (GARN). The RIS unit cells are first grouped in this scheme to reduce the pilot overhead. Then, the initial channel estimation is obtained using the LS algorithm, serving as the input to GARN. Since the GARN adopts the global attention mechanism, it can extract multi-channel features and enhance information fusion, improving the accuracy of channel estimation matrices. Experimental comparisons with multiple benchmark methods demonstrate the proposed scheme's excellent estimation performance and generalization ability. In the same year, [PERSON] et al. [91] developed two deep-learning-based schemes for RIS-aided mmWave MIMO systems, i.e., data-driven frequency-flat cascaded channel estimation (DD-FF-CE) and data-driven frequency-selective cascaded channel estimation (DD-FS-CE). For the DD-FF-CE, the authors turned channel estimation into a CS problem by leveraging the angular cascaded channel's double-structured sparsity and solving it using deep learning.