Nonlinear turbo equalization using context trees

Nargiz Kalantarova; Kyeongyeon Kim; Suleyman S. Kozat; Andrew C. Singer

doi:10.1109/ITA.2011.5743608

Nonlinear turbo equalization using context trees

Nargiz Kalantarova
, Kyeongyeon Kim
, Suleyman S. Kozat
, Andrew C. Singer

Office of the Dean of the College of Engineering and Applied Sciences

Koc University
University of Illinois at Urbana-Champaign

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

Abstract

In this paper, we study adaptive nonlinear turbo equalization to model the nonlinear dependency of a linear minimum mean square error (MMSE) equalizer on soft information from the decoder. To accomplish this, we introduce piecewise linear models based on context trees that can adaptively choose both the partition regions as well as the equalizer coefficients in each region independently, with the computational complexity of a single adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer that can choose both its regions as well as its filter parameters based on observing the whole data sequence in advance.

Original language	English
Title of host publication	2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings
Pages	378-382
Number of pages	5
DOIs	https://doi.org/10.1109/ITA.2011.5743608
State	Published - 2011
Event	2011 Information Theory and Applications Workshop, ITA 2011 - San Diego, CA, United States Duration: Feb 6 2011 → Feb 11 2011

Publication series

Name	2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings

Conference

Conference	2011 Information Theory and Applications Workshop, ITA 2011
Country/Territory	United States
City	San Diego, CA
Period	02/6/11 → 02/11/11

Keywords

context trees
decision feedback
nonlinear equalization
Turbo equalization

Access to Document

10.1109/ITA.2011.5743608

Cite this

@inproceedings{f16f4573063741cc98cf3255f9606d33,

title = "Nonlinear turbo equalization using context trees",

abstract = "In this paper, we study adaptive nonlinear turbo equalization to model the nonlinear dependency of a linear minimum mean square error (MMSE) equalizer on soft information from the decoder. To accomplish this, we introduce piecewise linear models based on context trees that can adaptively choose both the partition regions as well as the equalizer coefficients in each region independently, with the computational complexity of a single adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer that can choose both its regions as well as its filter parameters based on observing the whole data sequence in advance.",

keywords = "context trees, decision feedback, nonlinear equalization, Turbo equalization",

author = "Nargiz Kalantarova and Kyeongyeon Kim and Kozat, \{Suleyman S.\} and Singer, \{Andrew C.\}",

year = "2011",

doi = "10.1109/ITA.2011.5743608",

language = "English",

isbn = "9781457703614",

series = "2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings",

pages = "378--382",

booktitle = "2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings",

note = "2011 Information Theory and Applications Workshop, ITA 2011 ; Conference date: 06-02-2011 Through 11-02-2011",

}

Kalantarova, N, Kim, K, Kozat, SS & Singer, AC 2011, Nonlinear turbo equalization using context trees. in 2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings., 5743608, 2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings, pp. 378-382, 2011 Information Theory and Applications Workshop, ITA 2011, San Diego, CA, United States, 02/6/11. https://doi.org/10.1109/ITA.2011.5743608

Nonlinear turbo equalization using context trees. / Kalantarova, Nargiz; Kim, Kyeongyeon; Kozat, Suleyman S. et al.
2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings. 2011. p. 378-382 5743608 (2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings).

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

TY - GEN

T1 - Nonlinear turbo equalization using context trees

AU - Kalantarova, Nargiz

AU - Kim, Kyeongyeon

AU - Kozat, Suleyman S.

AU - Singer, Andrew C.

PY - 2011

Y1 - 2011

N2 - In this paper, we study adaptive nonlinear turbo equalization to model the nonlinear dependency of a linear minimum mean square error (MMSE) equalizer on soft information from the decoder. To accomplish this, we introduce piecewise linear models based on context trees that can adaptively choose both the partition regions as well as the equalizer coefficients in each region independently, with the computational complexity of a single adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer that can choose both its regions as well as its filter parameters based on observing the whole data sequence in advance.

AB - In this paper, we study adaptive nonlinear turbo equalization to model the nonlinear dependency of a linear minimum mean square error (MMSE) equalizer on soft information from the decoder. To accomplish this, we introduce piecewise linear models based on context trees that can adaptively choose both the partition regions as well as the equalizer coefficients in each region independently, with the computational complexity of a single adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer that can choose both its regions as well as its filter parameters based on observing the whole data sequence in advance.

KW - context trees

KW - decision feedback

KW - nonlinear equalization

KW - Turbo equalization

UR - https://www.scopus.com/pages/publications/79955781810

U2 - 10.1109/ITA.2011.5743608

DO - 10.1109/ITA.2011.5743608

M3 - Conference contribution

AN - SCOPUS:79955781810

SN - 9781457703614

T3 - 2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings

SP - 378

EP - 382

BT - 2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings

T2 - 2011 Information Theory and Applications Workshop, ITA 2011

Y2 - 6 February 2011 through 11 February 2011

ER -

Nonlinear turbo equalization using context trees

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Keywords

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