Model-based Deep Learning for Joint RIS Phase Shift Compression and WMMSE Beamforming

Abstract: A model-based deep learning (DL) architecture is proposed for reconfigurable intelligent surface (RIS)-assisted multi-user communications to reduce the overhead of transmitting phase shift information from the access point (AP) to the RIS controller. The phase shifts are computed at the AP, which has access to the channel state information, and then encoded into a compressed binary control message that is sent to the RIS controller for element configuration. To help reduce beamformer mismatches due to phase shift compression errors, the beamformer is updated using weighted minimum mean square error (WMMSE) based on the effective channel resulting from the actual (decompressed) RIS reflection coefficients. By unrolling the iterative WMMSE algorithm as part of the wireless communication informed DL architecture, joint phase shift compression and WMMSE beamforming can be trained end-to-end. Simulations show that accounting for phase shift compression errors during beamforming significantly improves the sum-rate performance, even when the number of control bits is lower than the number of RIS elements.