Efficient Iterative Linear Precoding Scheme for Downlink Massive MIMO Systems

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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Linear Precoding of Data and Artificial Noise in Secure Massive MIMO Systems

IEEE Transactions on Wireless Communications ◽

10.1109/twc.2015.2500578 ◽

2016 ◽

Vol 15 (3) ◽

pp. 2245-2261 ◽

Cited By ~ 101

Author(s):

Jun Zhu ◽

Robert Schober ◽

Vijay K. Bhargava

Keyword(s):

Massive Mimo ◽

Mimo Systems ◽

Artificial Noise ◽

Linear Precoding

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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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