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This paper proposes a low-complexity algorithm to calculate log likelihood ratios (LLRs) of coded bits, which is necessary for channel decoding in coded MIMO mobile communications. An approximate LLR needs to find a pair of transmitted signal candidates that can maximize the log likelihood function under a constraint that a coded bit is equal to either one or zero. The proposed algorithm can find such a pair simultaneously, whereas conventional ones find them individually. Specifically, the proposed method searches for such candidates in directions of the noise enhancement using the MMSE detection as a starting point. First, an inverse matrix which the MMSE weight matrix includes is obtained and then the power method derives eigenvectors of the inverse matrix as the directions of the noise enhancement. With some eigenvectors, one-dimensional search and hard decision are performed. From the resultant signals, the transmitted signal candidates to be required are selected on the basis of the log likelihood function. Computer simulations with 4*E _{b}*/

- Publication
- IEICE TRANSACTIONS on Communications Vol.E94-B No.1 pp.183-193

- Publication Date
- 2011/01/01

- Publicized

- Online ISSN
- 1745-1345

- DOI
- 10.1587/transcom.E94.B.183

- Type of Manuscript
- PAPER

- Category
- Wireless Communication Technologies

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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Liming ZHENG, Jooin WOO, Kazuhiko FUKAWA, Hiroshi SUZUKI, Satoshi SUYAMA, "Low-Complexity Algorithm for Log Likelihood Ratios in Coded MIMO Communications" in IEICE TRANSACTIONS on Communications,
vol. E94-B, no. 1, pp. 183-193, January 2011, doi: 10.1587/transcom.E94.B.183.

Abstract: This paper proposes a low-complexity algorithm to calculate log likelihood ratios (LLRs) of coded bits, which is necessary for channel decoding in coded MIMO mobile communications. An approximate LLR needs to find a pair of transmitted signal candidates that can maximize the log likelihood function under a constraint that a coded bit is equal to either one or zero. The proposed algorithm can find such a pair simultaneously, whereas conventional ones find them individually. Specifically, the proposed method searches for such candidates in directions of the noise enhancement using the MMSE detection as a starting point. First, an inverse matrix which the MMSE weight matrix includes is obtained and then the power method derives eigenvectors of the inverse matrix as the directions of the noise enhancement. With some eigenvectors, one-dimensional search and hard decision are performed. From the resultant signals, the transmitted signal candidates to be required are selected on the basis of the log likelihood function. Computer simulations with 4*E _{b}*/

URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.183/_p

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@ARTICLE{e94-b_1_183,

author={Liming ZHENG, Jooin WOO, Kazuhiko FUKAWA, Hiroshi SUZUKI, Satoshi SUYAMA, },

journal={IEICE TRANSACTIONS on Communications},

title={Low-Complexity Algorithm for Log Likelihood Ratios in Coded MIMO Communications},

year={2011},

volume={E94-B},

number={1},

pages={183-193},

abstract={This paper proposes a low-complexity algorithm to calculate log likelihood ratios (LLRs) of coded bits, which is necessary for channel decoding in coded MIMO mobile communications. An approximate LLR needs to find a pair of transmitted signal candidates that can maximize the log likelihood function under a constraint that a coded bit is equal to either one or zero. The proposed algorithm can find such a pair simultaneously, whereas conventional ones find them individually. Specifically, the proposed method searches for such candidates in directions of the noise enhancement using the MMSE detection as a starting point. First, an inverse matrix which the MMSE weight matrix includes is obtained and then the power method derives eigenvectors of the inverse matrix as the directions of the noise enhancement. With some eigenvectors, one-dimensional search and hard decision are performed. From the resultant signals, the transmitted signal candidates to be required are selected on the basis of the log likelihood function. Computer simulations with 4*E _{b}*/

keywords={},

doi={10.1587/transcom.E94.B.183},

ISSN={1745-1345},

month={January},}

Copy

TY - JOUR

TI - Low-Complexity Algorithm for Log Likelihood Ratios in Coded MIMO Communications

T2 - IEICE TRANSACTIONS on Communications

SP - 183

EP - 193

AU - Liming ZHENG

AU - Jooin WOO

AU - Kazuhiko FUKAWA

AU - Hiroshi SUZUKI

AU - Satoshi SUYAMA

PY - 2011

DO - 10.1587/transcom.E94.B.183

JO - IEICE TRANSACTIONS on Communications

SN - 1745-1345

VL - E94-B

IS - 1

JA - IEICE TRANSACTIONS on Communications

Y1 - January 2011

AB - This paper proposes a low-complexity algorithm to calculate log likelihood ratios (LLRs) of coded bits, which is necessary for channel decoding in coded MIMO mobile communications. An approximate LLR needs to find a pair of transmitted signal candidates that can maximize the log likelihood function under a constraint that a coded bit is equal to either one or zero. The proposed algorithm can find such a pair simultaneously, whereas conventional ones find them individually. Specifically, the proposed method searches for such candidates in directions of the noise enhancement using the MMSE detection as a starting point. First, an inverse matrix which the MMSE weight matrix includes is obtained and then the power method derives eigenvectors of the inverse matrix as the directions of the noise enhancement. With some eigenvectors, one-dimensional search and hard decision are performed. From the resultant signals, the transmitted signal candidates to be required are selected on the basis of the log likelihood function. Computer simulations with 4*E _{b}*/

ER -