This paper proposes the periodical successive over-relaxation (PSOR)-Jacobi algorithm for minimum mean squared error (MMSE) detection of multiple-input multiple-output (MIMO) signals. The proposed algorithm has the advantages of two conventional methods. One is the Jacobi method, which is an iterative method for solving linear equations and is suitable for parallel implementation. The Jacobi method is thus a promising candidate for high-speed simultaneous linear equation solvers for the MMSE detector. The other is the Chebyshev PSOR method, which has recently been shown to accelerate the convergence speed of linear fixed-point iterations. We compare the convergence performance of the PSOR-Jacobi algorithm with that of conventional algorithms via computer simulation. The results show that the PSOR-Jacobi algorithm achieves faster convergence without increasing computational complexity, and higher detection performance for a fixed number of iterations. This paper also proposes an efficient computation method of inverse matrices using the PSOR-Jacobi algorithm. The results of computer simulation show that the PSOR-Jacobi algorithm also accelerates the computation of inverse matrix.
Asahi MIZUKOSHI
Nagoya Institute of Technology
Ayano NAKAI-KASAI
Nagoya Institute of Technology
Tadashi WADAYAMA
Nagoya Institute of Technology
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Asahi MIZUKOSHI, Ayano NAKAI-KASAI, Tadashi WADAYAMA, "PSOR-Jacobi Algorithm for Accelerated MMSE MIMO Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E107-A, no. 3, pp. 486-492, March 2024, doi: 10.1587/transfun.2023TAP0004.
Abstract: This paper proposes the periodical successive over-relaxation (PSOR)-Jacobi algorithm for minimum mean squared error (MMSE) detection of multiple-input multiple-output (MIMO) signals. The proposed algorithm has the advantages of two conventional methods. One is the Jacobi method, which is an iterative method for solving linear equations and is suitable for parallel implementation. The Jacobi method is thus a promising candidate for high-speed simultaneous linear equation solvers for the MMSE detector. The other is the Chebyshev PSOR method, which has recently been shown to accelerate the convergence speed of linear fixed-point iterations. We compare the convergence performance of the PSOR-Jacobi algorithm with that of conventional algorithms via computer simulation. The results show that the PSOR-Jacobi algorithm achieves faster convergence without increasing computational complexity, and higher detection performance for a fixed number of iterations. This paper also proposes an efficient computation method of inverse matrices using the PSOR-Jacobi algorithm. The results of computer simulation show that the PSOR-Jacobi algorithm also accelerates the computation of inverse matrix.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023TAP0004/_p
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@ARTICLE{e107-a_3_486,
author={Asahi MIZUKOSHI, Ayano NAKAI-KASAI, Tadashi WADAYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={PSOR-Jacobi Algorithm for Accelerated MMSE MIMO Detection},
year={2024},
volume={E107-A},
number={3},
pages={486-492},
abstract={This paper proposes the periodical successive over-relaxation (PSOR)-Jacobi algorithm for minimum mean squared error (MMSE) detection of multiple-input multiple-output (MIMO) signals. The proposed algorithm has the advantages of two conventional methods. One is the Jacobi method, which is an iterative method for solving linear equations and is suitable for parallel implementation. The Jacobi method is thus a promising candidate for high-speed simultaneous linear equation solvers for the MMSE detector. The other is the Chebyshev PSOR method, which has recently been shown to accelerate the convergence speed of linear fixed-point iterations. We compare the convergence performance of the PSOR-Jacobi algorithm with that of conventional algorithms via computer simulation. The results show that the PSOR-Jacobi algorithm achieves faster convergence without increasing computational complexity, and higher detection performance for a fixed number of iterations. This paper also proposes an efficient computation method of inverse matrices using the PSOR-Jacobi algorithm. The results of computer simulation show that the PSOR-Jacobi algorithm also accelerates the computation of inverse matrix.},
keywords={},
doi={10.1587/transfun.2023TAP0004},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - PSOR-Jacobi Algorithm for Accelerated MMSE MIMO Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 486
EP - 492
AU - Asahi MIZUKOSHI
AU - Ayano NAKAI-KASAI
AU - Tadashi WADAYAMA
PY - 2024
DO - 10.1587/transfun.2023TAP0004
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E107-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2024
AB - This paper proposes the periodical successive over-relaxation (PSOR)-Jacobi algorithm for minimum mean squared error (MMSE) detection of multiple-input multiple-output (MIMO) signals. The proposed algorithm has the advantages of two conventional methods. One is the Jacobi method, which is an iterative method for solving linear equations and is suitable for parallel implementation. The Jacobi method is thus a promising candidate for high-speed simultaneous linear equation solvers for the MMSE detector. The other is the Chebyshev PSOR method, which has recently been shown to accelerate the convergence speed of linear fixed-point iterations. We compare the convergence performance of the PSOR-Jacobi algorithm with that of conventional algorithms via computer simulation. The results show that the PSOR-Jacobi algorithm achieves faster convergence without increasing computational complexity, and higher detection performance for a fixed number of iterations. This paper also proposes an efficient computation method of inverse matrices using the PSOR-Jacobi algorithm. The results of computer simulation show that the PSOR-Jacobi algorithm also accelerates the computation of inverse matrix.
ER -