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

A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints