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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pendekatan pengesanan isyarat kerumitan rendah berdasarkan algoritma Kaczmarz (KA) dicadangkan untuk merealisasikan pengesanan ralat min kuasa dua minimum (MMSE) secara berulang untuk sistem keluaran berbilang input besar-besaran (MIMO) pautan atas. Walaupun KA digunakan untuk penyongsangan matriks mudah, pengesanan MMSE memerlukan pengiraan matriks Gram dengan kerumitan yang tinggi. Untuk mengelakkan pengiraan matriks Gram, matriks tambahan yang setara digunakan untuk pengesanan MMSE berasaskan KA. Selain itu, anggaran awal yang menjanjikan dan kaedah anggaran untuk mengira maklumat output lembut digunakan untuk mempercepatkan lagi kadar penumpuan dan mengurangkan kerumitan. Keputusan simulasi menunjukkan bahawa pendekatan yang dicadangkan mengatasi siri Neumann yang dicadangkan baru-baru ini, kecerunan konjugat, dan kaedah Gauss-Seidel dalam kerumitan dan prestasi kadar ralat. Sementara itu, keputusan pelaksanaan FPGA mengesahkan bahawa kaedah cadangan kami boleh mengira anggaran songsang dengan kerumitan yang rendah dengan cekap.
Zhuojun LIANG
Shanghai Jiao Tong University
Chunhui DING
Shanghai Jiao Tong University
Guanghui HE
Shanghai Jiao Tong University
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Salinan
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
Salinan
@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},}
Salinan
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 -