Approximative Matrix Inversion Based Linear Precoding for Massive MIMO Systems

Modified Conjugate Gradient Algorithms for Gram Matrix Inversion of Massive MIMO Downlink Linear Precoding

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1351.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 2834-2840

Keyword(s):

Conjugate Gradient ◽

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Low Complexity ◽

Matrix Inversion ◽

Gram Matrix ◽

Linear Precoding ◽

Inversion Algorithm ◽

The Matrix

This paper deals with various low complexity algorithms for higher order matrix inversion involved in massive MIMO system precoder design. The performance of massive MIMO systems is optimized by the process of precoding which is divided into linear and nonlinear. Nonlinear precoding techniques are most complex precoding techniques irrespective of its performance. Hence, linear precoding is generally preferred in which the complexity is mainly contributed by matrix inversion algorithm. To solve this issue, Krylov subspace algorithm such as Conjugate Gradient (CG) was considered to be the best choice of replacement for exact matrix inversions. But CG enforces a condition that the matrix needs to be Symmetric Positive Definite (SPD). If the matrix to be inverted is asymmetric then CG fails to converge. Hence in this paper, a novel approach for the low complexity inversion of asymmetric matrices is proposed by applying two different versions of CG algorithms- Conjugate Gradient Squared (CGS) and Bi-conjugate Gradient (Bi-CG). The convergence behavior and BER performance of these two algorithms are compared with the existing CG algorithm. The results show that these two algorithms outperform CG in terms of convergence speed and relative residue.

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Joint Newton Iteration and Neumann Series Method of Convergence-Accelerating Matrix Inversion Approximation in Linear Precoding for Massive MIMO Systems

Mathematical Problems in Engineering ◽

10.1155/2016/1745808 ◽

2016 ◽

Vol 2016 ◽

pp. 1-5 ◽

Cited By ~ 6

Author(s):

Lin Shao ◽

Yunxiao Zu

Keyword(s):

Massive Mimo ◽

Mimo Systems ◽

Neumann Series ◽

Convergence Condition ◽

Newton Iteration ◽

Matrix Inversion ◽

Linear Precoding ◽

Series Method ◽

Large Numbers ◽

Speed Up

Due to large numbers of antennas and users, matrix inversion is complicated in linear precoding techniques for massive MIMO systems. Several approximated matrix inversion methods, including the Neumann series, have been proposed to reduce the complexity. However, the Neumann series does not converge fast enough. In this paper, to speed up convergence, a new joint Newton iteration and Neumann series method is proposed, with the first iteration result of Newton iteration method being employed to reconstruct the Neumann series. Then, a high probability convergence condition is established, which can offer useful guidelines for practical massive MIMO systems. Finally, simulation examples are given to demonstrate that the new joint Newton iteration and Neumann series method has a faster convergence rate compared to the previous Neumann series, with almost no increase in complexity when the iteration number is greater than or equal to 2.

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A Low-Complexity Linear Precoding Algorithm Based on Jacobi Method for Massive MIMO Systems

2018 IEEE 87th Vehicular Technology Conference (VTC Spring) ◽

10.1109/vtcspring.2018.8417575 ◽

2018 ◽

Cited By ~ 3

Author(s):

Juan Minango ◽

Celso de Almeida

Keyword(s):

Massive Mimo ◽

Mimo Systems ◽

Low Complexity ◽

Jacobi Method ◽

Linear Precoding

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Linear Precoding for Massive MIMO Systems with IQ Imbalance

Machine Learning and Intelligent Communications - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ◽

10.1007/978-3-319-73564-1_48 ◽

2018 ◽

pp. 484-493

Author(s):

Juan Liu ◽

Jianxin Dai ◽

Chonghu Cheng ◽

Zhiliang Huang

Keyword(s):

Massive Mimo ◽

Mimo Systems ◽

Linear Precoding ◽

Iq Imbalance

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PCA-Aided Linear Precoding in Massive MIMO Systems with Imperfect CSI

Wireless Communications and Mobile Computing ◽

10.1155/2020/3425952 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Van-Khoi Dinh ◽

Minh-Tuan Le ◽

Vu-Duc Ngo ◽

Chi-Hieu Ta

Keyword(s):

Principal Component Analysis ◽

Computational Complexity ◽

Massive Mimo ◽

Mimo Systems ◽

Minimum Mean Square Error ◽

Principal Component ◽

Low Complexity ◽

Component Analysis ◽

Linear Precoding ◽

Block Diagonalization

In this paper, a low-complexity linear precoding algorithm based on the principal component analysis technique in combination with the conventional linear precoders, called Principal Component Analysis Linear Precoder (PCA-LP), is proposed for massive MIMO systems. The proposed precoder consists of two components: the first one minimizes the interferences among neighboring users and the second one improves the system performance by utilizing the Principal Component Analysis (PCA) technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its low-complexity lattice reduction-aided regularized block diagonalization using zero forcing precoding (LC-RBD-LR-ZF) and lower computational complexity than the PCA-aided Minimum Mean Square Error combination with Block Diagonalization (PCA-MMSE-BD) counterparts while its bit error rate (BER) performance is comparable to those of the LC-RBD-LR-ZF and PCA-MMSE-BD ones.

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