Maximum likelihood blind deconvolution for sparse systems

Steffen Barembruch, Anna Scaglione, Eric Moulines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In recent years many sparse estimation methods, also known as compressed sensing, have been developed for channel identification problems in digital communications. However, all these methods presume the transmitted sequence of symbols to be known at the receiver, i.e. in form of a training sequence. We consider blind identification of the channel based on maximum likelihood (ML) estimation via the EM algorithm incorporating a sparsity constraint in the maximization step. We apply this algorithm to a linear modulation scheme on a doubly-selective channel model.

Original languageEnglish (US)
Title of host publication2010 2nd International Workshop on Cognitive Information Processing, CIP2010
Pages69-74
Number of pages6
DOIs
StatePublished - Nov 22 2010
Externally publishedYes
Event2010 2nd International Workshop on Cognitive Information Processing, CIP2010 - Elba Island, Italy
Duration: Jun 14 2010Jun 16 2010

Publication series

Name2010 2nd International Workshop on Cognitive Information Processing, CIP2010

Other

Other2010 2nd International Workshop on Cognitive Information Processing, CIP2010
CountryItaly
CityElba Island
Period6/14/106/16/10

Keywords

  • Compressive Sensing
  • Deconvolution
  • Multipath channels
  • Smoothing methods

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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    Barembruch, S., Scaglione, A., & Moulines, E. (2010). Maximum likelihood blind deconvolution for sparse systems. In 2010 2nd International Workshop on Cognitive Information Processing, CIP2010 (pp. 69-74). [5604139] (2010 2nd International Workshop on Cognitive Information Processing, CIP2010). https://doi.org/10.1109/CIP.2010.5604139