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 | IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC |
| DOIs | |
| State | Published - 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 |
Other
| Other | 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010 |
|---|---|
| Country | Morocco |
| City | Marrakech |
| Period | 6/20/10 → 6/23/10 |
Fingerprint
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Information Systems
Cite this
A sparse EM algorithm for blind and semi-blind identification of doubly selective OFDM channels. / Barembruch, S.; Moulines, Eric; Scaglione, Anna.
IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC. 2010. 5670864.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
Y1 - 2010
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
BT - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
ER -