Joint Sampling and Recovery of Correlated Sources

Nir Shlezinger, Salman Salamatian, Yonina C. Eldar, Muriel Medard

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


Sampling enables physical signals to be processed using digital hardware. When multiple signals are sampled, the spatial correlation between them may be utilized to reduce the overall reconstruction error. In this work we study joint sampling and reconstruction of multiple correlated stochastic sources, exploiting their correlation to improve recovery. We derive the achievable reconstruction error and the corresponding sampling system for arbitrary sampling rates and spectral structures. The proposed system minimizes the error when sampling below the Nyquist rate by preserving only the most dominant spatial eigenmodes aliased to each frequency. Using this characterization, we obtain sufficient conditions for error free recovery. We also discuss a distributed sampling setting, where each signal is acquired separately, while reconstruction is performed jointly. We characterize conditions under which distributed sampling performs as well as joint sampling. Our numerical results illustrate that joint sampling can achieve negligible reconstruction error using low sampling rates when the signals exhibit notable spatial correlation, and demonstrate that properly exploiting this correlation can dramatically improve reconstruction accuracy.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538692912
StatePublished - 1 Jul 2019
Externally publishedYes
Event2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France
Duration: 7 Jul 201912 Jul 2019

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2019 IEEE International Symposium on Information Theory, ISIT 2019

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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