Compressed sensing and linear codes over real numbers

Published

Conference Paper

Compressed sensing (CS) is a relatively new area of signal processing and statistics that focuses on signal reconstruction from a small number of linear (e.g., dot product) measurements. In this paper, we analyze CS using tools from coding theory because CS can also be viewed as syndrome-based source coding of sparse vectors using linear codes over real numbers. While coding theory does not typically deal with codes over real numbers, there is actually a very close relationship between CS and error-correcting codes over large discrete alphabets. This connection leads naturally to new reconstruction methods and analysis. In some cases, the resulting methods provably require many fewer measurements than previous approaches.

Full Text

Duke Authors

Cited Authors

  • Zhang, F; Pfister, HD

Published Date

  • October 6, 2008

Published In

  • 2008 Information Theory and Applications Workshop Conference Proceedings, Ita

Start / End Page

  • 558 - 561

International Standard Book Number 10 (ISBN-10)

  • 1424426707

International Standard Book Number 13 (ISBN-13)

  • 9781424426706

Digital Object Identifier (DOI)

  • 10.1109/ITA.2008.4601055

Citation Source

  • Scopus