SINR analysis in persymmetric adaptive processing

Jun Liu; Hongwei Liu; Bo Chen; Xiang Gen Xia

doi:10.1109/SSP.2016.7551756

SINR analysis in persymmetric adaptive processing

Jun Liu, Hongwei Liu, Bo Chen, Xiang Gen Xia

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

Abstract

We study the normalized output signal-to-interference-plus-noise ratio (SINR) of a sample matrix inversion (SMI) beamformer with exploiting a priori information on persymmetric structures in the received signal. An exact expression for the expectation of the normalized output SINR (i.e., average S-INR loss) of the persymmetric SMI beamformer is obtained. Simulation results reveal that the exploitation of the persymmetric structure is equivalent to doubling the amount of training data.

Original language	English
Title of host publication	2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467378024
DOIs	https://doi.org/10.1109/SSP.2016.7551756
Publication status	Published - 24 Aug 2016
Externally published	Yes
Event	19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain Duration: 25 Jun 2016 → 29 Jun 2016

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2016-August

Conference

Conference	19th IEEE Statistical Signal Processing Workshop, SSP 2016
Country/Territory	Spain
City	Palma de Mallorca
Period	25/06/16 → 29/06/16

Keywords

Persymmetry
SINR loss
sample matrix inversion (SMI) beamformer

Access to Document

10.1109/SSP.2016.7551756

Cite this

Liu, J., Liu, H., Chen, B., & Xia, X. G. (2016). SINR analysis in persymmetric adaptive processing. In 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016 Article 7551756 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/SSP.2016.7551756

@inproceedings{26212a8d7db04cc790e5221a46c778cf,

title = "SINR analysis in persymmetric adaptive processing",

abstract = "We study the normalized output signal-to-interference-plus-noise ratio (SINR) of a sample matrix inversion (SMI) beamformer with exploiting a priori information on persymmetric structures in the received signal. An exact expression for the expectation of the normalized output SINR (i.e., average S-INR loss) of the persymmetric SMI beamformer is obtained. Simulation results reveal that the exploitation of the persymmetric structure is equivalent to doubling the amount of training data.",

keywords = "Persymmetry, SINR loss, sample matrix inversion (SMI) beamformer",

author = "Jun Liu and Hongwei Liu and Bo Chen and Xia, {Xiang Gen}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 19th IEEE Statistical Signal Processing Workshop, SSP 2016 ; Conference date: 25-06-2016 Through 29-06-2016",

year = "2016",

month = aug,

day = "24",

doi = "10.1109/SSP.2016.7551756",

language = "English",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

booktitle = "2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016",

address = "United States",

}

Liu, J, Liu, H, Chen, B & Xia, XG 2016, SINR analysis in persymmetric adaptive processing. in 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016., 7551756, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2016-August, IEEE Computer Society, 19th IEEE Statistical Signal Processing Workshop, SSP 2016, Palma de Mallorca, Spain, 25/06/16. https://doi.org/10.1109/SSP.2016.7551756

SINR analysis in persymmetric adaptive processing. / Liu, Jun; Liu, Hongwei; Chen, Bo et al.
2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016. IEEE Computer Society, 2016. 7551756 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2016-August).

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

TY - GEN

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AU - Liu, Jun

AU - Liu, Hongwei

AU - Chen, Bo

AU - Xia, Xiang Gen

PY - 2016/8/24

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N2 - We study the normalized output signal-to-interference-plus-noise ratio (SINR) of a sample matrix inversion (SMI) beamformer with exploiting a priori information on persymmetric structures in the received signal. An exact expression for the expectation of the normalized output SINR (i.e., average S-INR loss) of the persymmetric SMI beamformer is obtained. Simulation results reveal that the exploitation of the persymmetric structure is equivalent to doubling the amount of training data.

AB - We study the normalized output signal-to-interference-plus-noise ratio (SINR) of a sample matrix inversion (SMI) beamformer with exploiting a priori information on persymmetric structures in the received signal. An exact expression for the expectation of the normalized output SINR (i.e., average S-INR loss) of the persymmetric SMI beamformer is obtained. Simulation results reveal that the exploitation of the persymmetric structure is equivalent to doubling the amount of training data.

KW - Persymmetry

KW - SINR loss

KW - sample matrix inversion (SMI) beamformer

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U2 - 10.1109/SSP.2016.7551756

DO - 10.1109/SSP.2016.7551756

M3 - Conference contribution

AN - SCOPUS:84987879690

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

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PB - IEEE Computer Society

T2 - 19th IEEE Statistical Signal Processing Workshop, SSP 2016

Y2 - 25 June 2016 through 29 June 2016

ER -

SINR analysis in persymmetric adaptive processing

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Keywords

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