The expectation and sparse maximization algorithm

Steffen Barembruch, Anna Scaglione, Eric Moulines

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method-the expectation-sparse-maximization (ESpaM) algorithm-for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.

Original languageEnglish (US)
Pages (from-to)317-329
Number of pages13
JournalJournal of Communications and Networks
Volume12
Issue number4
DOIs
StatePublished - Aug 2010
Externally publishedYes

Keywords

  • Compressive sensing (CS)
  • Deconvolution
  • Multipath channels
  • Smoothing methods

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'The expectation and sparse maximization algorithm'. Together they form a unique fingerprint.

  • Cite this