Blind Modulo Analog-to-Digital Conversion

Amir Weiss, Everest Huang, Or Ordentlich, Gregory W. Wornell

Research output: Contribution to journalArticlepeer-review

Abstract

In a growing number of applications, there is a need to digitize signals whose spectral characteristics are challenging for traditional analog-to-digital converters (ADCs). Examples, among others, include systems where the ADC must acquire at once a very wide but sparsely and dynamically occupied bandwidth supporting diverse services, as well as systems where the signal of interest is subject to strong narrowband co-channel interference. In such scenarios, the resolution requirements can be prohibitively high. As an alternative, the recently proposed <italic>modulo-ADC</italic> architecture can in principle require dramatically fewer bits in the conversion to obtain the target fidelity, but requires that information about the spectrum be known and explicitly taken into account by the analog and digital processing in the converter, which is frequently impractical. To address this limitation, we develop a <italic>blind</italic> version of the architecture that requires no such knowledge in the converter, without sacrificing performance. In particular, it features an automatic modulo-level adjustment and a fully adaptive modulo unwrapping mechanism, allowing it to asymptotically match the characteristics of the unknown input signal. In addition to detailed analysis, simulations demonstrate the attractive performance characteristics in representative settings.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalIEEE Transactions on Signal Processing
DOIs
StateAccepted/In press - 2022

Keywords

  • Analog-digital conversion
  • Dynamic range
  • Gain control
  • Narrowband
  • Quantization (signal)
  • Spectrogram
  • Standards
  • adaptive filtering
  • automatic gain control
  • blind signal processing
  • data conversion
  • least-mean-squares

Fingerprint

Dive into the research topics of 'Blind Modulo Analog-to-Digital Conversion'. Together they form a unique fingerprint.

Cite this