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

Efficient Expectation Propagation Massive MIMO Detector With Neumann-Series Approximation

2020 ◽  
Vol 67 (10) ◽  
pp. 1924-1928
Author(s):  
Xiaosi Tan ◽  
Weihong Xu ◽  
Yaping Zhang ◽  
Zaichen Zhang ◽  
Xiaohu You ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Neumann Series ◽  
Expectation Propagation ◽  
Series Approximation

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

A Low-Complexity Massive MIMO Detection Based on Approximate Expectation Propagation

2019 ◽  
Vol 68 (8) ◽  
pp. 7260-7272 ◽  
Author(s):  
Xiaosi Tan ◽  
Yeong-Luh Ueng ◽  
Zaichen Zhang ◽  
Xiaohu You ◽  
Chuan Zhang
Keyword(s):  
Massive Mimo ◽  
Low Complexity ◽  
Mimo Detection ◽  
Expectation Propagation

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

Improving Approximate Expectation Propagation Massive MIMO Detector with Deep Learning

2021 ◽  
pp. 1-1
Author(s):  
Yingmeng Ge ◽  
Xiaosi Tan ◽  
Zhenhao Ji ◽  
Zaichen Zhang ◽  
Xiaohu You ◽  
...  
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Expectation Propagation

scholarly journals High-Efficiency Expectation Propagation Detector for High-Order Massive MIMO Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125225-125239
Author(s):  
Guoqiang Yao ◽  
Guiwu Yang ◽  
Jianhao Hu ◽  
Chao Fei
Keyword(s):  
Massive Mimo ◽  
High Efficiency ◽  
Mimo Systems ◽  
High Order ◽  
Expectation Propagation

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.

Sign in / Sign up

Export Citation Format

Share Document