On The Log-Likelihood Ratio Evaluation of CWCU Linear and Widely Linear MMSE Data Estimators

Oliver Lang; Mario Huemer; Christian Hofbauer

doi:10.1109/ACSSC.2016.7869120

On The Log-Likelihood Ratio Evaluation of CWCU Linear and Widely Linear MMSE Data Estimators

Oliver Lang, Mario Huemer, Christian Hofbauer

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

Abstract

In soft decoding, log-likelihood ratios (LLRs) are calculated from estimated data symbols. Data symbols from proper constellation diagrams such as QPSK are often estimated using the linear minimum mean square error (LMMSE) estimator. We prove that the recently introduced component-wise conditionally unbiased (CWCU) LMMSE estimator results in the very same LLRs as the LMMSE estimator for typical model assumptions. For improper constellation diagrams such as 8-QAM, we show that the widely linear versions of the LMMSE and the CWCU LMMSE estimator also yield identical LLRs. In that case, the CWCU estimator allows to reduce the complexity of the LLR determination.

Original language	English
Title of host publication	2016 50th Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE Computer Society
Pages	633-637
Number of pages	5
ISBN (Print)	978-1-5386-3955-9
DOIs	https://doi.org/10.1109/ACSSC.2016.7869120
Publication status	Published - 9 Nov 2016
Externally published	Yes
Event	2016 50th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, USA Duration: 6 Nov 2016 → 9 Nov 2016

Conference

Conference	2016 50th Asilomar Conference on Signals, Systems and Computers
Period	6/11/16 → 9/11/16

Keywords

Bayes methods
Covariance matrices
Constellation diagram
Phase shift keying
Mean square error methods
Estimation
Quadrature amplitude modulation

Access to Document

10.1109/ACSSC.2016.7869120

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7869120

Cite this

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title = "On The Log-Likelihood Ratio Evaluation of CWCU Linear and Widely Linear MMSE Data Estimators",

abstract = "In soft decoding, log-likelihood ratios (LLRs) are calculated from estimated data symbols. Data symbols from proper constellation diagrams such as QPSK are often estimated using the linear minimum mean square error (LMMSE) estimator. We prove that the recently introduced component-wise conditionally unbiased (CWCU) LMMSE estimator results in the very same LLRs as the LMMSE estimator for typical model assumptions. For improper constellation diagrams such as 8-QAM, we show that the widely linear versions of the LMMSE and the CWCU LMMSE estimator also yield identical LLRs. In that case, the CWCU estimator allows to reduce the complexity of the LLR determination.",

keywords = "Bayes methods, Covariance matrices, Constellation diagram, Phase shift keying, Mean square error methods, Estimation, Quadrature amplitude modulation",

author = "Oliver Lang and Mario Huemer and Christian Hofbauer",

year = "2016",

month = nov,

day = "9",

doi = "10.1109/ACSSC.2016.7869120",

language = "English",

isbn = "978-1-5386-3955-9",

pages = "633--637",

booktitle = "2016 50th Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

address = "United States",

note = "2016 50th Asilomar Conference on Signals, Systems and Computers ; Conference date: 06-11-2016 Through 09-11-2016",

}

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T1 - On The Log-Likelihood Ratio Evaluation of CWCU Linear and Widely Linear MMSE Data Estimators

AU - Lang, Oliver

AU - Huemer, Mario

AU - Hofbauer, Christian

PY - 2016/11/9

Y1 - 2016/11/9

N2 - In soft decoding, log-likelihood ratios (LLRs) are calculated from estimated data symbols. Data symbols from proper constellation diagrams such as QPSK are often estimated using the linear minimum mean square error (LMMSE) estimator. We prove that the recently introduced component-wise conditionally unbiased (CWCU) LMMSE estimator results in the very same LLRs as the LMMSE estimator for typical model assumptions. For improper constellation diagrams such as 8-QAM, we show that the widely linear versions of the LMMSE and the CWCU LMMSE estimator also yield identical LLRs. In that case, the CWCU estimator allows to reduce the complexity of the LLR determination.

AB - In soft decoding, log-likelihood ratios (LLRs) are calculated from estimated data symbols. Data symbols from proper constellation diagrams such as QPSK are often estimated using the linear minimum mean square error (LMMSE) estimator. We prove that the recently introduced component-wise conditionally unbiased (CWCU) LMMSE estimator results in the very same LLRs as the LMMSE estimator for typical model assumptions. For improper constellation diagrams such as 8-QAM, we show that the widely linear versions of the LMMSE and the CWCU LMMSE estimator also yield identical LLRs. In that case, the CWCU estimator allows to reduce the complexity of the LLR determination.

KW - Bayes methods

KW - Covariance matrices

KW - Constellation diagram

KW - Phase shift keying

KW - Mean square error methods

KW - Estimation

KW - Quadrature amplitude modulation

UR - https://ieeexplore.ieee.org/document/7869120/

U2 - 10.1109/ACSSC.2016.7869120

DO - 10.1109/ACSSC.2016.7869120

M3 - Conference Paper

SN - 978-1-5386-3955-9

SP - 633

EP - 637

BT - 2016 50th Asilomar Conference on Signals, Systems and Computers

PB - IEEE Computer Society

T2 - 2016 50th Asilomar Conference on Signals, Systems and Computers

Y2 - 6 November 2016 through 9 November 2016

ER -

On The Log-Likelihood Ratio Evaluation of CWCU Linear and Widely Linear MMSE Data Estimators

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