A sparse EM algorithm for blind and semi-blind identification of doubly selective OFDM channels

S. Barembruch, Eric Moulines, Anna Scaglione

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

2 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 an OFDM transmission over a doubly-selective multipath channel with strong Doppler and delay spread.

Original languageEnglish (US)
Title of host publication2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010 - Marrakech, Morocco
Duration: Jun 20 2010Jun 23 2010

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

Other

Other2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010
CountryMorocco
CityMarrakech
Period6/20/106/23/10

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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    Barembruch, S., Moulines, E., & Scaglione, A. (2010). A sparse EM algorithm for blind and semi-blind identification of doubly selective OFDM channels. In 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010 [5670864] (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). https://doi.org/10.1109/SPAWC.2010.5670864