  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.
