scholarly journals Fast Converging Iterative Precoding for Massive MIMO Systems: An Accelerated Weighted Neumann Series-Steepest Descent Approach

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 50244-50255
Author(s):  
Qian Deng ◽  
Xiaopeng Liang ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Chao Dong ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Neumann Series ◽  
Steepest Descent

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

scholarly journals An Efficient Estimation of the Number of Optimal Iterations for GS Pre-coding in Downlink Massive MIMO Systems

2020 ◽  
Vol 10 (23) ◽  
pp. 8735
Author(s):  
Jae-Hyun Ro ◽  
Woon-Sang Lee ◽  
Hyun-Sun Hwang ◽  
Duckdong Hwang ◽  
Young-Hwan You ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Signal Transmission ◽  
Multiple Input Multiple Output ◽  
Closed Form Solution ◽  
Neumann Series ◽  
Form Solution ◽  
Efficient Estimation ◽  
Input Multiple Output ◽  
Estimation Scheme

This paper proposes an estimation scheme of the number iterations for optimal Gauss–Seidel (GS) pre-coding in the downlink massive multiple input multiple output (MIMO) systems for the first time. The number of iterations in GS pre-coding is one of the key parameters and should be estimated accurately prior to signal transmission in the downlink systems. For efficient estimation without presentations of the closed-form solution for the GS pre-coding symbols, the proposed estimation scheme uses the relative method which calculates the normalized Euclidean distance (NED) between consecutive GS solutions by using the property of the monotonic decrease function of the GS solutions. Additionally, an efficient initial solution for the GS pre-coding is proposed as a two term Neumann series (NS) based on the stair matrix for improving the accuracy of estimation and accelerating the convergence rate of the GS solution. The evaluated estimation performances verify high accuracy in the downlink massive MIMO systems even in low loading factors. In addition, an additional complexity for estimating the number of the optimal iterations is nearly negligible.

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.

Fast Converging Weighted Neumann Series Precoding for Massive MIMO Systems

2018 ◽  
Vol 7 (2) ◽  
pp. 154-157 ◽  
Author(s):  
Betty Nagy ◽  
Maha Elsabrouty ◽  
Salwa Elramly
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Neumann Series

A Deeply Fused Detection Algorithm Based on Steepest Descent and Non-Stationary Richardson Iteration for Massive MIMO Systems

2020 ◽  
Vol 24 (12) ◽  
pp. 2742-2745
Author(s):  
Mengdan Lou ◽  
Jiaming Tu ◽  
Dewu Shu ◽  
Muhammad Abu Bakar ◽  
Guanghui He
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Detection Algorithm ◽  
Steepest Descent
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