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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.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E107-A No.3 pp.486-492

- Publication Date
- 2024/03/01

- Publicized
- 2023/08/04

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.2023TAP0004

- Type of Manuscript
- Special Section PAPER (Special Section on Information Theory and Its Applications)

- Category
- Communication Theory and Systems

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 -