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 language | English (US) |
|---|---|
| Pages (from-to) | 317-329 |
| Number of pages | 13 |
| Journal | Journal of Communications and Networks |
| Volume | 12 |
| Issue number | 4 |
| State | Published - Aug 2010 |
| Externally published | Yes |
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Keywords
- Compressive sensing (CS)
- Deconvolution
- Multipath channels
- Smoothing methods
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
Cite this
The expectation and sparse maximization algorithm. / Barembruch, Steffen; Scaglione, Anna; Moulines, Eric.
In: Journal of Communications and Networks, Vol. 12, No. 4, 08.2010, p. 317-329.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The expectation and sparse maximization algorithm
AU - Barembruch, Steffen
AU - Scaglione, Anna
AU - Moulines, Eric
PY - 2010/8
Y1 - 2010/8
N2 - 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.
AB - 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.
KW - Compressive sensing (CS)
KW - Deconvolution
KW - Multipath channels
KW - Smoothing methods
UR - http://www.scopus.com/inward/record.url?scp=77956588222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956588222&partnerID=8YFLogxK
M3 - Article
VL - 12
SP - 317
EP - 329
JO - Journal of Communications and Networks
JF - Journal of Communications and Networks
SN - 1229-2370
IS - 4
ER -