  In this paper, we investigate a cascaded channel estimation method for a
millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system
aided by a reconfigurable intelligent surface (RIS) with the BS equipped with
low-resolution analog-to-digital converters (ADCs), where the BS and the RIS
are both equipped with a uniform planar array (UPA). Due to the sparse property
of mmWave channel, the channel estimation can be solved as a compressed sensing
(CS) problem. However, the low-resolution quantization cause severe information
loss of signals, and traditional CS algorithms are unable to work well. To
recovery the signal and the sparse angular domain channel from quantization, we
introduce Bayesian inference and efficient vector approximate message passing
(VAMP) algorithm to solve the quantize output CS problem. To further improve
the efficiency of the VAMP algorithm, a Fast Fourier Transform (FFT) based fast
computation method is derived. Simulation results demonstrate the effectiveness
and the accuracy of the proposed cascaded channel estimation method for the
RIS-aided mmWave massive MIMO system with few-bit ADCs. Furthermore, the
proposed channel estimation method can reach an acceptable performance gap
between the low-resolution ADCs and the infinite ADCs for the low
signal-to-noise ratio (SNR), which implies the applicability of few-bit ADCs in
practice.
