Abstract
This letter investigates the uplink data detection for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output (MIMO) systems with low-resolution quantization at the base stations (BSs). By exploiting the statistical characteristics of the user-to-RIS and RIS-to-BS channels and the central limit theorem (CLT), we first demonstrate that the components in the effective channel are approximately Gaussian distributed under propagation in sub-6 GHz bands. Based on the bilinear generalized approximate message passing (BiG-AMP) algorithm, we then provide a quantization-aware approach to estimate the effective channel and recover transmit symbols. Finally, numerical results show that the revised BiG-AMP technique can improve the performance of channel estimation and data detection with affordable computational complexity.
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