A Low-Complexity Signal Detection Approach in Uplink Massive MIMO Systems
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.7 pp.1115-1119
- Publication Date
- 2018/07/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.1115
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Zhuojun LIANG
Shanghai Jiao Tong University
Chunhui DING
Shanghai Jiao Tong University
Guanghui HE
Shanghai Jiao Tong University
Keyword
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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 -
English
