Hardware-Limited Task-Based Quantization

Nir Shlezinger, Yonina C. Eldar, Miguel R.D. Rodrigues

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

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.

Original languageEnglish
Article number8805173
Pages (from-to)5223-5238
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume67
Issue number20
DOIs
StatePublished - 15 Oct 2019
Externally publishedYes

Keywords

  • Quantization
  • analog-to-digital conversion

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