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Reconfigurable intelligent surface (RIS) constitutes an essential andpromising paradigm that relies programmable wireless environment and providescapability for space-intensive communications, due to the use of low-costmassive reflecting elements over the entire surfaces of man-made structures.However, accurate channel estimation is a fundamental technical prerequisite toachieve the huge performance gains from RIS. By leveraging the low rankstructure of RIS channels, three practical residual neural networks, namedconvolutional blind denoising network, convolutional denoising generativeadversarial networks and multiple residual dense network, are proposed toobtain accurate channel state information, which can reflect the impact ofdifferent methods on the estimation performance. Simulation results reveal theevolution direction of these three methods and reveal their superiorperformance compared with existing benchmark schemes.

Multiple Residual Dense Networks for Reconfigurable Intelligent Surfaces Cascaded Channel Estimation