scholarly journals Approximate Neumann Series or Exact Matrix Inversion for Massive MIMO?

2017 ◽  
Author(s):  
Oscar Gustafsson ◽  
Erik Bertilsson ◽  
Johannes Klasson ◽  
Carl Ingemarsson
Keyword(s):  
Massive Mimo ◽  
Neumann Series ◽  
Matrix Inversion ◽  
Exact Matrix

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

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
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.

scholarly journals Efficient hybrid Neumann series based MMSE assisted detection for 5G and beyond massive MIMO systems

2020 ◽  
Vol 14 (22) ◽  
pp. 4142-4151
Author(s):  
Kiran Khurshid ◽  
Muhammad Imran ◽  
Adnan Ahmed Khan ◽  
Imran Rashid ◽  
Haroon Siddiqui
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Neumann Series

Approximate Expectation Propagation Massive MIMO Detector with Weighted Neumann-Series

2020 ◽  
pp. 1-1
Author(s):  
Xiaosi Tan ◽  
Huijun Han ◽  
Muhao Li ◽  
Kai Sun ◽  
Yongming Huang ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Neumann Series ◽  
Expectation Propagation

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.

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