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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Vydáno v:IEEE communications letters Ročník 23; číslo 4; s. 748 - 751
Hlavní autoři: Jin, Fangli, Liu, Qiufeng, Liu, Hao, Wu, Peng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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