Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits

Alon Kipnis, Yonina C. Eldar, Andrea J. Goldsmith

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Processing, storing, and communicating information that originates as an analog signal involves converting this information to bits. This conversion can be described by the combined effect of sampling and quantization, as shown in Figure 1. The digital representation is achieved by first sampling the analog signal to represent it by a set of discretetime samples and then quantizing these samples to a finite number of bits. Traditionally, these two operations are considered separately. The sampler is designed to minimize the information loss due to sampling based on characteristics of the continuoustime input. The quantizer is designed to represent the samples as accurately as possible, subject to a constraint on the number of bits that can be used in the representation. The goal of this article is to revisit this paradigm by illuminating the dependency between these two operations. In particular, we explore the requirements of the sampling system subject to the constraints on the available number of bits for storing, communicating, or processing the analog information.

Original languageEnglish (US)
Pages (from-to)16-39
Number of pages24
JournalIEEE Signal Processing Magazine
Volume35
Issue number3
DOIs
StatePublished - May 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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