IEICE TRANSACTIONS on Communications
Maximum-Likelihood Precoder Selection for ML Detector in MIMO-OFDM Systems
Summary :
Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E95-B No.5 pp.1856-1859
- Publication Date
- 2012/05/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E95.B.1856
- Type of Manuscript
- LETTER
- Category
- Wireless Communication Technologies
Authors
Keyword
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Sung-Yoon JUNG, Jong-Ho LEE, Daeyoung PARK, "Maximum-Likelihood Precoder Selection for ML Detector in MIMO-OFDM Systems" in IEICE TRANSACTIONS on Communications,
vol. E95-B, no. 5, pp. 1856-1859, May 2012, doi: 10.1587/transcom.E95.B.1856.
Abstract: Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E95.B.1856/_p
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@ARTICLE{e95-b_5_1856,
author={Sung-Yoon JUNG, Jong-Ho LEE, Daeyoung PARK, },
journal={IEICE TRANSACTIONS on Communications},
title={Maximum-Likelihood Precoder Selection for ML Detector in MIMO-OFDM Systems},
year={2012},
volume={E95-B},
number={5},
pages={1856-1859},
abstract={Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.},
keywords={},
doi={10.1587/transcom.E95.B.1856},
ISSN={1745-1345},
month={May},}
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TY - JOUR
TI - Maximum-Likelihood Precoder Selection for ML Detector in MIMO-OFDM Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 1856
EP - 1859
AU - Sung-Yoon JUNG
AU - Jong-Ho LEE
AU - Daeyoung PARK
PY - 2012
DO - 10.1587/transcom.E95.B.1856
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E95-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2012
AB - Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.
ER -
English
