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

S. Barembruch; Eric Moulines; Anna Scaglione

doi:10.1109/SPAWC.2010.5670864

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 proceeding › Conference 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 language	English (US)
Title of host publication	2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010
DOIs	https://doi.org/10.1109/SPAWC.2010.5670864
State	Published - Dec 1 2010
Externally published	Yes
Event	2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010 - Marrakech, Morocco Duration: Jun 20 2010 → Jun 23 2010

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

Other

Other	2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010
Country/Territory	Morocco
City	Marrakech
Period	6/20/10 → 6/23/10

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

Access to Document

10.1109/SPAWC.2010.5670864

Cite this

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

A sparse EM algorithm for blind and semi-blind identification of doubly selective OFDM channels. / Barembruch, S.; Moulines, Eric; Scaglione, Anna.

2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010. 2010. 5670864 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010, Marrakech, Morocco, 6/20/10. https://doi.org/10.1109/SPAWC.2010.5670864

Barembruch S, Moulines E, Scaglione A. 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. 2010. 5670864. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). https://doi.org/10.1109/SPAWC.2010.5670864

@inproceedings{6ee6367058ad47cbb9bebe1f79e05f22,

title = "A sparse EM algorithm for blind and semi-blind identification of doubly selective OFDM channels",

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.",

author = "S. Barembruch and Eric Moulines and Anna Scaglione",

year = "2010",

month = dec,

day = "1",

doi = "10.1109/SPAWC.2010.5670864",

language = "English (US)",

isbn = "9781424469901",

series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",

booktitle = "2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010",

note = "2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010 ; Conference date: 20-06-2010 Through 23-06-2010",

}

TY - GEN

T1 - A sparse EM algorithm for blind and semi-blind identification of doubly selective OFDM channels

AU - Barembruch, S.

AU - Moulines, Eric

AU - Scaglione, Anna

PY - 2010/12/1

Y1 - 2010/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78751512028&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78751512028&partnerID=8YFLogxK

U2 - 10.1109/SPAWC.2010.5670864

DO - 10.1109/SPAWC.2010.5670864

M3 - Conference contribution

AN - SCOPUS:78751512028

SN - 9781424469901

T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

BT - 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010

T2 - 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010

Y2 - 20 June 2010 through 23 June 2010

ER -

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

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this