In this paper, we investigate channel estimation for reconfigurable intelligent surface (RIS) empowered millimeter-wave (mmWave) multi-user single-input multiple-output communication systems using low-resolution quantization. Due to the high cost and power consumption of analog-to-digital converters (ADCs) in large antenna arrays and for wide signal bandwidths, designing mmWave systems with low-resolution ADCs is beneficial. To tackle this issue, we propose a channel estimation design using task-based quantization that considers the underlying hybrid analog and digital architecture in order to improve the system performance under finite bit-resolution constraints. Our goal is to accomplish a channel estimation task that minimizes the mean squared error distortion between the true and estimated channel. We develop two types of channel estimators: a cascaded channel estimator for an RIS with purely passive elements, and an estimator for the separate RIS-related channels that leverages additional information from a few semi-passive elements at the RIS capable of processing the received signals with radio frequency chains. Numerical results demonstrate that the proposed channel estimation designs exploiting task-based quantization outperform purely digital methods and can effectively approach the performance of a system with unlimited resolution ADCs. Furthermore, the proposed channel estimators are shown to be superior to baselines with small training overhead.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems require accurate channel estimation for effective beamforming. Conventional compressed sensing (CS) algorithms utilize the inherent sparsity information of channels but suffer from basis mismatch problems for on-grid methods and high computational complexity for off-grid methods. Recently, generative model-based CS frameworks have shown promising results by directly learning the distribution of channel realizations, eliminating the need for prior sparsity information. However, these approaches rely on non-convex optimization in latent space that may converge to suboptimal solutions and exhibit generalization problems across diverse channel environments. Moreover, existing generative approaches have not adequately addressed the quantization effects of practical finite-resolution analog-to-digital converters (ADCs). To address these limitations, we propose a novel generative CS (GCS) algorithm using a conditional variational autoencoder (CVAE) that learns fundamental path components of mmWave MIMO channels rather than complete channel realizations while considering finite-resolution quantization. The CVAE is trained on single-path measurements with spatial frequency conditioning variables, enabling generalization across diverse channel environments without requiring environment-specific training data. Our algorithm directly encodes quantized received pilot signals and angular conditioning variables into latent representations, which are then fed into a decoder to output reconstructions conditioned on the angular parameters, with the decoder trained to be robust to perturbations from additive and quantization noise. The algorithm sequentially identifies path components by searching through spatial frequency codebooks with different conditioning values and applying gradient-based refinement for off-grid accuracy. Numerical results demonstrate that the proposed GCS algorithm outperforms conventional CS algorithms across various scenarios.

Due to the simultaneous downlink and uplink transmissions in reconfigurable intelligent surface (RIS)-empowered frequency division duplexing (FDD) communication systems, it is necessary to design the RIS phase shifts to balance the performance of both directions at the same time. Focusing on a single-user multiple-input multiple-output system, we aim to maximize a weighted sum-rate for the downlink and uplink. To address the resulting non-convex optimization problem, we employ an alternating optimization (AO) algorithm, which includes two techniques for optimizing the phase shifts at the RIS. A manifold optimization-based algorithm is applied for the first technique, and a lower-complexity AO approach is developed for the second. Our numerical results demonstrate that the proposed algorithms lead to substantial enhancement of the entire system compared to existing baseline schemes.

This paper investigates reconfigurable intelligent surface (RIS)-aided frequency division duplexing (FDD) communication systems. Since the downlink and uplink signals are simultaneously transmitted in FDD, the phase shifts at the RIS should be designed to support both transmissions. Considering a single-user multiple-input multiple-output system, we formulate a weighted sum-rate maximization problem to jointly maximize the downlink and uplink system performance. To tackle the non-convex optimization problem, we adopt an alternating optimization (AO) algorithm, in which two phase shift optimization techniques are developed to handle the unit-modulus constraints induced by the reflection coefficients at the RIS. The first technique exploits the manifold optimization-based algorithm, while the second uses a lower-complexity AO approach. Numerical results verify that the proposed techniques rapidly converge to local optima and significantly improve the overall system performance compared to existing benchmark schemes.