  Reconfigurable intelligent surface (RIS) has become a promising technology to
improve wireless communication in recent years. It steers the incident signals
to create a favorable propagation environment by controlling the reconfigurable
passive elements with less hardware cost and lower power consumption. In this
paper, we consider a RIS-aided multiuser multiple-input single-output downlink
communication system. We aim to maximize the weighted sum-rate of all users by
joint optimizing the active beamforming at the access point and the passive
beamforming vector of the RIS elements. Unlike most existing works, we consider
the more practical situation with the discrete phase shifts and imperfect
channel state information (CSI). Specifically, for the situation that the
discrete phase shifts and perfect CSI are considered, we first develop a deep
quantization neural network (DQNN) to simultaneously design the active and
passive beamforming while most reported works design them alternatively. Then,
we propose an improved structure (I-DQNN) based on DQNN to simplify the
parameters decision process when the control bits of each RIS element are
greater than 1 bit. Finally, we extend the two proposed DQNN-based algorithms
to the case that the discrete phase shifts and imperfect CSI are considered
simultaneously. Our simulation results show that the two DQNN-based algorithms
have better performance than traditional algorithms in the perfect CSI case,
and are also more robust in the imperfect CSI case.
