Richardson Method Based Linear Precoding with Low Complexity for Massive MIMO Systems

2015 ◽  
Author(s):  
Zhaohua Lu ◽  
Jiaqi Ning ◽  
Yi Zhang ◽  
Tian Xie ◽  
Wenqian Shen
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
Linear Precoding ◽  
Richardson Method

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

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.

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

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.

Low-complexity and fast-convergence linear precoding based on modified SOR for massive MIMO systems

2020 ◽  
Vol 107 ◽  
pp. 102864
Author(s):  
Yang Liu ◽  
Yuting Li ◽  
Xiaodong Cheng ◽  
Yinbo Lian ◽  
Yongjun Jia ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
Fast Convergence ◽  
Linear Precoding

A Near-Optimal Iterative Linear Precoding With Low Complexity for Massive MIMO Systems

2019 ◽  
Vol 23 (6) ◽  
pp. 1105-1108 ◽  
Author(s):  
Yang Liu ◽  
Jinhong Liu ◽  
Qiong Wu ◽  
Yinghui Zhang ◽  
Minglu Jin
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
Linear Precoding
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