Reconfigurable intelligent surface (RIS) technology is a promising enabler for 6G communication systems due to its ability to reconfigure wireless propagation environments. However, as a passive device, RIS requires significant pilot overhead for accurate channel estimation. Moreover, the integration of RIS with multiple-input multiple-output (MIMO) systems further exacerbates power consumption and hardware costs. To address these challenges, this paper investigates RIS-assisted millimeter-wave (mmWave) MIMO systems with low-resolution analog-to-digital converters (ADCs). Exploiting the inherent sparsity of mmWave channels and considering the distortion introduced by low-resolution quantization, we propose a compressive sensing (CS)-based channel estimation scheme. Furthermore, to mitigate the effects of angular leakage, we introduce an energy capture orthogonal matching pursuit (ECOMP) algorithm. Simulation results demonstrate that the proposed scheme not only improves channel estimation accuracy but also reduces pilot overhead and power consumption, while maintaining enhanced stability in high signal-to-noise ratio (SNR) regimes.
Fu et al. (2026) studied this question.