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
Une approche de détection de signal de faible complexité basée sur l'algorithme de Kaczmarz (KA) est proposée pour réaliser de manière itérative une détection d'erreur quadratique moyenne minimale (MMSE) pour les systèmes massifs à entrées multiples et sorties multiples (MIMO) de liaison montante. Alors que KA est utilisé pour une simple inversion de matrice, la détection MMSE nécessite le calcul de la matrice de Gram avec une grande complexité. Afin d'éviter le calcul de la matrice de Gram, une matrice augmentée équivalente est appliquée à la détection MMSE basée sur KA. De plus, une estimation initiale prometteuse et une méthode approximative pour calculer les informations de sortie logicielle sont utilisées pour accélérer davantage le taux de convergence et réduire la complexité. Les résultats de la simulation démontrent que l'approche proposée surpasse les méthodes de série de Neumann, de gradient conjugué et de Gauss-Seidel récemment proposées en termes de complexité et de taux d'erreur. Parallèlement, les résultats de l'implémentation du FPGA confirment que la méthode proposée peut calculer efficacement l'inverse approximatif avec une faible complexité.
Zhuojun LIANG
Shanghai Jiao Tong University
Chunhui DING
Shanghai Jiao Tong University
Guanghui HE
Shanghai Jiao Tong University
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Zhuojun LIANG, Chunhui DING, Guanghui HE, "A Low-Complexity Signal Detection Approach in Uplink Massive MIMO Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 7, pp. 1115-1119, July 2018, doi: 10.1587/transfun.E101.A.1115.
Abstract: A low-complexity signal detection approach based on the Kaczmarz algorithm (KA) is proposed to iteratively realize minimum mean square error (MMSE) detection for uplink massive multiple-input multiple-output (MIMO) systems. While KA is used for straightforward matrix inversion, the MMSE detection requires the computation of the Gram matrix with high complexity. In order to avoid the Gram matrix computation, an equivalent augmented matrix is applied to KA-based MMSE detection. Moreover, promising initial estimation and an approximate method to compute soft-output information are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results demonstrate that the proposed approach outperforms the recently proposed Neumann series, conjugate gradient, and Gauss-Seidel methods in complexity and error-rate performance. Meanwhile, the FPGA implementation results confirm that our proposed method can efficiently compute the approximate inverse with low complexity.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1115/_p
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@ARTICLE{e101-a_7_1115,
author={Zhuojun LIANG, Chunhui DING, Guanghui HE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Low-Complexity Signal Detection Approach in Uplink Massive MIMO Systems},
year={2018},
volume={E101-A},
number={7},
pages={1115-1119},
abstract={A low-complexity signal detection approach based on the Kaczmarz algorithm (KA) is proposed to iteratively realize minimum mean square error (MMSE) detection for uplink massive multiple-input multiple-output (MIMO) systems. While KA is used for straightforward matrix inversion, the MMSE detection requires the computation of the Gram matrix with high complexity. In order to avoid the Gram matrix computation, an equivalent augmented matrix is applied to KA-based MMSE detection. Moreover, promising initial estimation and an approximate method to compute soft-output information are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results demonstrate that the proposed approach outperforms the recently proposed Neumann series, conjugate gradient, and Gauss-Seidel methods in complexity and error-rate performance. Meanwhile, the FPGA implementation results confirm that our proposed method can efficiently compute the approximate inverse with low complexity.},
keywords={},
doi={10.1587/transfun.E101.A.1115},
ISSN={1745-1337},
month={July},}
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TY - JOUR
TI - A Low-Complexity Signal Detection Approach in Uplink Massive MIMO Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1115
EP - 1119
AU - Zhuojun LIANG
AU - Chunhui DING
AU - Guanghui HE
PY - 2018
DO - 10.1587/transfun.E101.A.1115
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2018
AB - A low-complexity signal detection approach based on the Kaczmarz algorithm (KA) is proposed to iteratively realize minimum mean square error (MMSE) detection for uplink massive multiple-input multiple-output (MIMO) systems. While KA is used for straightforward matrix inversion, the MMSE detection requires the computation of the Gram matrix with high complexity. In order to avoid the Gram matrix computation, an equivalent augmented matrix is applied to KA-based MMSE detection. Moreover, promising initial estimation and an approximate method to compute soft-output information are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results demonstrate that the proposed approach outperforms the recently proposed Neumann series, conjugate gradient, and Gauss-Seidel methods in complexity and error-rate performance. Meanwhile, the FPGA implementation results confirm that our proposed method can efficiently compute the approximate inverse with low complexity.
ER -