Task-Based Quantization for Channel Estimation in RIS Empowered MmWave Systems

2025-10-16

AI Summary

Low-resolution analog-to-digital converters (ADCs) severely degrade channel estimation accuracy in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user MIMO systems. Method: This paper proposes a task-oriented quantized channel estimation framework featuring a cascaded estimator and a decoupled estimator leveraging semi-passive RIS elements, integrated within a hybrid analog-digital architecture to enable efficient signal processing under stringent constraints on the number of radio frequency (RF) chains. Crucially, it introduces a task-driven quantization mechanism that jointly optimizes bit allocation across ADCs and the channel estimation process. Results: Experiments demonstrate that the proposed scheme achieves significantly higher estimation accuracy than conventional fully digital approaches—approaching the performance upper bound of infinite-resolution ADC systems—while drastically reducing training overhead. It thus effectively balances hardware cost, power consumption, and communication performance.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Estimating channels in RIS-aided mmWave systems with low-resolution quantization
• Designing task-based quantization to overcome finite bit-resolution constraints
• Developing channel estimators for passive and semi-passive RIS configurations

Innovation

• Task-based quantization for channel estimation
• Hybrid analog-digital architecture optimization
• Cascaded and semi-passive RIS estimators