Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes.

翻译：重新配置智能表面(RIS)是依赖可编程无线环境、提供空间密集通信能力的基本和有希望的范例,因为使用低成本大规模反映人为结构整个表面的各种要素,但是,准确的信道估计是RIS取得巨大性能收益的基本技术先决条件。通过利用RIS频道的低级结构,提议建立三个实际的残余神经网络,称为革命性盲目分解网络、遗传对抗网络和多个残余密集网络,以获取准确的频道状态信息,这些信息能够反映不同方法对估计绩效的影响。模拟结果揭示了这三种方法的演变方向,并揭示了它们与现有基准计划相比的优异性。