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

TL;DR

This work tackles channel estimation for RIS-aided mmWave MU-SIMO under low-resolution ADCs. It introduces task-based quantization that co-designs analog/digital processing to minimize the MSE of channel estimates, presenting two schemes: (i) cascaded estimation for purely passive RISs and (ii) a two-stage approach using semi-passive RIS elements to separately estimate the BS-RIS and UE-RIS channels. By optimally selecting the analog combiner, digital processor, and ADC thresholds, the methods achieve near-MMSE performance with substantially fewer quantization bits and training overhead than conventional digital approaches. The results demonstrate significant reductions in hardware complexity while maintaining high estimation accuracy, highlighting practical viability for RIS deployments in dense mmWave networks.

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.

Figures (12)

• Figure 1: An example of an uplink RIS-aided mmWave MU-SIMO system with semi-passive elements. There are $N$ BS antennas, $K$ UEs with a single antenna, and $L$ total RIS elements.
• Figure 2: The uplink time-domain transmission design for estimating the cascaded channel.
• Figure 3: An example of a hardware-limited task-based quantization system.
• Figure 4: Architecture for cascaded channel estimation using hardware-limited task-based quantization.
• Figure 5: Architecture for estimating the individual RIS channels using hardware-limited task-based quantization.

Theorems & Definitions (14)

• Lemma 1
• proof
• Lemma 2
• proof
• Proposition 1
• proof
• Lemma 3
• proof
• Lemma 4
• proof