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Machine Learning-Based Compensation Methods for Weight Matrices of SVD-MIMO
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Summary :
This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.12 pp.1441-1454
- Publication Date
- 2023/12/01
- Publicized
- 2023/07/24
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2023EBP3033
- Type of Manuscript
- PAPER
- Category
- Antennas and Propagation
Authors
https://orcid.org/0000-0002-2482-7964
Keyword
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Kiminobu MAKINO, Takayuki NAKAGAWA, Naohiko IAI, "Machine Learning-Based Compensation Methods for Weight Matrices of SVD-MIMO" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 12, pp. 1441-1454, December 2023, doi: 10.1587/transcom.2023EBP3033.
Abstract: This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2023EBP3033/_p
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@ARTICLE{e106-b_12_1441,
author={Kiminobu MAKINO, Takayuki NAKAGAWA, Naohiko IAI, },
journal={IEICE TRANSACTIONS on Communications},
title={Machine Learning-Based Compensation Methods for Weight Matrices of SVD-MIMO},
year={2023},
volume={E106-B},
number={12},
pages={1441-1454},
abstract={This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.},
keywords={},
doi={10.1587/transcom.2023EBP3033},
ISSN={1745-1345},
month={December},}
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TY - JOUR
TI - Machine Learning-Based Compensation Methods for Weight Matrices of SVD-MIMO
T2 - IEICE TRANSACTIONS on Communications
SP - 1441
EP - 1454
AU - Kiminobu MAKINO
AU - Takayuki NAKAGAWA
AU - Naohiko IAI
PY - 2023
DO - 10.1587/transcom.2023EBP3033
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2023
AB - This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.
ER -
English
