A Low Complexity Signal Detection Scheme Based on Improved Newton Iteration for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems need to handle a large number of matrix inversion operations during the signal detection process. Several methods have been proposed to avoid exact matrix inversion in massive MIMO systems, which can be roughly divided into approximation methods...

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Published in:	IEEE communications letters Vol. 23; no. 4; pp. 748 - 751
Main Authors:	Jin, Fangli, Liu, Qiufeng, Liu, Hao, Wu, Peng
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Approximation Complexity Complexity theory Computer simulation Convergence Estimation Iterative methods Jacobian matrices Massive MIMO Mathematical analysis Matrix algebra matrix inversion Matrix methods Methods MIMO (control systems) MIMO communication Neumann series Newton iteration Newton method Newton methods Series expansion Signal detection Signal processing
ISSN:	1089-7798, 1558-2558
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Abstract	Massive multiple-input multiple-output (MIMO) systems need to handle a large number of matrix inversion operations during the signal detection process. Several methods have been proposed to avoid exact matrix inversion in massive MIMO systems, which can be roughly divided into approximation methods and iterative methods. In this letter, we first introduce the relationship between the two types of signal detection methods. Then, an improved Newton iteration method is proposed on the basis of the relationship. And by converting the matrix-matrix product into the matrix-vector product, the computational complexity is substantially reduced. Finally, numerical simulations further verify that the proposed Newton method outperforms Neumann series expansion and the existing Newton method, and can approach the performance of minimum mean square error (MMSE) method within a few iterations, regardless of whether the base station can obtain perfect channel state information or not.
AbstractList	Massive multiple-input multiple-output (MIMO) systems need to handle a large number of matrix inversion operations during the signal detection process. Several methods have been proposed to avoid exact matrix inversion in massive MIMO systems, which can be roughly divided into approximation methods and iterative methods. In this letter, we first introduce the relationship between the two types of signal detection methods. Then, an improved Newton iteration method is proposed on the basis of the relationship. And by converting the matrix-matrix product into the matrix-vector product, the computational complexity is substantially reduced. Finally, numerical simulations further verify that the proposed Newton method outperforms Neumann series expansion and the existing Newton method, and can approach the performance of minimum mean square error (MMSE) method within a few iterations, regardless of whether the base station can obtain perfect channel state information or not.
Author	Liu, Hao Liu, Qiufeng Wu, Peng Jin, Fangli
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SubjectTerms	Approximation Complexity Complexity theory Computer simulation Convergence Estimation Iterative methods Jacobian matrices Massive MIMO Mathematical analysis Matrix algebra matrix inversion Matrix methods Methods MIMO (control systems) MIMO communication Neumann series Newton iteration Newton method Newton methods Series expansion Signal detection Signal processing
Title	A Low Complexity Signal Detection Scheme Based on Improved Newton Iteration for Massive MIMO Systems
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