  Reconfigurable intelligent surfaces (RISs) have recently received widespread
attention in the field of wireless communication. An RIS can be controlled to
reflect incident waves from the transmitter towards the receiver; a feature
that is believed to fundamentally contribute to beyond 5G wireless technology.
The typical RIS consists of entirely passive elements, which requires the
high-dimensional channel estimation to be done elsewhere. Therefore, in this
paper, we present a semi-passive large-scale RIS architecture equipped with
only a small fraction of simplified receiver units with only 1-bit
quantization. Based on this architecture, we first propose an alternating
direction method of multipliers (ADMM)-based approach to recover the training
signals at the passive RIS elements, We then obtain the global channel by
combining a channel sparsification step with the generalized approximate
message passing (GAMP) algorithm. Our proposed scheme exploits both the
sparsity and low-rankness properties of the channel in the joint
spatial-frequency domain of a wideband mmWave multiple-input-multiple-output
(MIMO) communication system. Simulation results show that the proposed
algorithm can significantly reduce the pilot signaling needed for accurate
channel estimation and outperform previous methods, even with fewer receiver
units.
