The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article propose une méthode basée sur l'inférence bayésienne variationnelle (VBI) de faible complexité pour l'estimation massive des canaux de liaison descendante à entrées multiples et sorties multiples (MIMO). La corrélation temporelle du côté de l'utilisateur mobile est exploitée conjointement pour améliorer les performances d'estimation de canal. La clé du succès de la méthode proposée est la factorisation indépendante des colonnes imposée dans le cadre VBI. Puisque nous séparons l'inférence bayésienne pour chaque vecteur colonne de signal d'intérêt, la complexité de calcul de la méthode proposée est considérablement réduite. De plus, la corrélation temporelle est automatiquement découplée pour faciliter la dérivation de règles de mise à jour pour la corrélation temporelle elle-même. Les résultats de simulation illustrent l’amélioration substantielle des performances obtenue par la méthode proposée.
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Chen JI, Shun WANG, Haijun FU, "Low-Complexity VBI-Based Channel Estimation for Massive MIMO Systems" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 5, pp. 600-607, May 2022, doi: 10.1587/transcom.2021EBP3064.
Abstract: This paper proposes a low-complexity variational Bayesian inference (VBI)-based method for massive multiple-input multiple-output (MIMO) downlink channel estimation. The temporal correlation at the mobile user side is jointly exploited to enhance the channel estimation performance. The key to the success of the proposed method is the column-independent factorization imposed in the VBI framework. Since we separate the Bayesian inference for each column vector of signal-of-interest, the computational complexity of the proposed method is significantly reduced. Moreover, the temporal correlation is automatically uncoupled to facilitate the updating rule derivation for the temporal correlation itself. Simulation results illustrate the substantial performance improvement achieved by the proposed method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3064/_p
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@ARTICLE{e105-b_5_600,
author={Chen JI, Shun WANG, Haijun FU, },
journal={IEICE TRANSACTIONS on Communications},
title={Low-Complexity VBI-Based Channel Estimation for Massive MIMO Systems},
year={2022},
volume={E105-B},
number={5},
pages={600-607},
abstract={This paper proposes a low-complexity variational Bayesian inference (VBI)-based method for massive multiple-input multiple-output (MIMO) downlink channel estimation. The temporal correlation at the mobile user side is jointly exploited to enhance the channel estimation performance. The key to the success of the proposed method is the column-independent factorization imposed in the VBI framework. Since we separate the Bayesian inference for each column vector of signal-of-interest, the computational complexity of the proposed method is significantly reduced. Moreover, the temporal correlation is automatically uncoupled to facilitate the updating rule derivation for the temporal correlation itself. Simulation results illustrate the substantial performance improvement achieved by the proposed method.},
keywords={},
doi={10.1587/transcom.2021EBP3064},
ISSN={1745-1345},
month={May},}
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TY - JOUR
TI - Low-Complexity VBI-Based Channel Estimation for Massive MIMO Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 600
EP - 607
AU - Chen JI
AU - Shun WANG
AU - Haijun FU
PY - 2022
DO - 10.1587/transcom.2021EBP3064
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2022
AB - This paper proposes a low-complexity variational Bayesian inference (VBI)-based method for massive multiple-input multiple-output (MIMO) downlink channel estimation. The temporal correlation at the mobile user side is jointly exploited to enhance the channel estimation performance. The key to the success of the proposed method is the column-independent factorization imposed in the VBI framework. Since we separate the Bayesian inference for each column vector of signal-of-interest, the computational complexity of the proposed method is significantly reduced. Moreover, the temporal correlation is automatically uncoupled to facilitate the updating rule derivation for the temporal correlation itself. Simulation results illustrate the substantial performance improvement achieved by the proposed method.
ER -