信道估计是通信系统中一项关键的技术，涉及评估信号在传输过程中经历的信道特性，以便接收端能够有效地对接收到的信号进行处理和恢复。为提高视距信道遮挡通信下的通信系统质量，使用智能超表面来辅助现有通信系统。智能超表面辅助的无线通信系统中，除了基站和用户之间的视距信道外，同时包含基站到智能超表面和智能超表面到用户之间的级联信道。当前信道估计方法基本上利用传统算法进行估计，为了解决智能超表面辅助多用户系统中复杂统计分布的级联信道估计精度低和计算复杂度高的问题，文中提出了一种基于传统算法和深度学习算法相结合的信道估计算法。利用传统算法的可解释性和深度学习算法的高性能特性，在卷积网络基础上，提出了一种基于残差密集网络（RDN）的去噪方法。首先按照系统参数模拟生成真实环境的数据集，使用传统最小二乘法（LS）进行信道粗估计，并将信道看作二维含噪图像；其次采用密集块（RDB）充分提取噪声数据局部特征，并使用多路卷积和残差网络对数据进行特征融合；最后通过已训练模型对数据进行在线估计，并得到去噪信道。文中从信道的估计精度对所提算法进行验证，在Rician信道模型上进行理论公式推导和系统仿真分析。仿真结果表明，与传统算法相比，文中所提出的算法提高了信道估计精度。

Channel estimation is a key technology in communication systems, which involves evaluating the channel characteristics experienced by signals during transmission, so that the receiver can effectively process and recover the received signals. In order to improve the quality of the communication system under the LOS channel occlusion communication, this study used an intelligent hypersurface to assist the existing communication system. In the smart hypersurface assisted wireless communication system, in addition to the line of sight channel between the base station and the user, there are also cascaded channels from the base station to the smart hypersurface and from the smart hypersurface to the user. The current channel estimation methods basically use traditional algorithms to estimate. In order to solve the problems of low accuracy and high computational complexity of the cascaded channel estimation with complex statistical distribution in the intelligent hypersurface assisted multi-user system, this paper proposed a channel estimation algorithm based on the combination of traditional algorithm and deep learning algorithm. The interpretability of traditional algorithms and the high performance of deep learning algorithms were utilized. On the basis of convolutional network, a denoising method based on residual dense network (RDN) was proposed. Firstly, the data set of the real environment was generated according to the simulation of the system parameters. And the traditional least squares (LS) method was used to rough estimate the channel, and the channel was regarded as a two-dimensional noisy image. Secondly, dense blocks (RDB) were used to fully extract the local features of noise data, and multi-channel convolution and residual network were used to fuse the data features. Finally, the trained model was used to estimate the data online and obtain the denoising channel. The proposed algorithm was verified from the channel estimation accuracy, and theoretical formula derivation and system simulation analysis were carried out on the Rician channel model. Simulation results show that the proposed algorithm improves the accuracy of channel estimation compared with the traditional algorithm.