Hardware-Limited Task-Based Quantization

TL;DR

This paper investigates how to design hardware-limited task-based quantization systems using serial scalar ADCs to efficiently recover parameters from signals, achieving near-optimal performance with limited bits.

Contribution

It introduces a framework for designing hardware-limited task-based quantizers and analyzes their performance, demonstrating near-optimal results in practical signal processing tasks.

Findings

Hardware-limited systems can approach vector quantizer performance.

Task-aware design reduces quantization error significantly.

Effective with small quantization bit budgets.

Abstract

Quantization plays a critical role in digital signal processing systems. Quantizers are typically designed to obtain an accurate digital representation of the input signal, operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). In this work, we study hardware-limited task-based quantization, where a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter vector underlying the input signal. We propose hardware-limited task-based quantization systems for a fixed and finite quantization resolution, and characterize their achievable distortion. We then apply the analysis to the practical setups of channel estimation and eigen-spectrum recovery from quantized measurements. Our results illustrate that properly designed hardware-limited systems can approach the optimal performance achievable with vector quantizers, and that by taking the underlying task into account, the quantization error can be made negligible with a relatively small number of bits.