Low-Complexity VBI-Based Channel Estimation for Massive MIMO Systems
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.5 pp.600-607
- Publication Date
- 2022/05/01
- Publicized
- 2021/11/11
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBP3064
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
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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 -
English
