Channel Estimation for RIS-Aided MIMO Systems with Low-Resolution ADCs via Three-Dimensional Decoupled Atomic Norm Minimization

Reconfigurable intelligent surfaces (RISs) have revolutionized the wireless propagation environment, proving instrumental across various communication scenarios. However, their integration significantly increases the system complexity, posing substantial challenges for channel estimation. This paper focuses on developing a computationally efficient channel estimation method for RIS-aided MIMO systems equipped with low-resolution analog-to-digital converters (ADCs). As the effective cascaded channel exhibits a three-dimensional sparse angular structure, we propose a novel cascaded channel estimation method based on three-dimensional decoupled atomic norm minimization. By integrating low-bit quantization constraints, this approach reformulates the estimation problem as a semidefinite programming problem, with angles estimated via Vandermonde decomposition of the resultant Toeplitz matrices. Then, the path complex gains are determined using least squares after pairing the estimated angles. Extensive simulation results validate that the proposed method accurately estimates the cascaded channel from the low-bit quantized data and offers a significant reduction in computational cost compared to existing methodologies.