Low complexity detection algorithm based on optimized Neumann series for massive MIMO system  

作　　者：Qu Tuosi Cao Haiyan Xu Fangmin Wang Xiumin 

机构地区：[1]School of Communication Engineering,Hangzhou Dianzi University [2]College of Information Engineering,China Jiliang University

出　　处：《The Journal of China Universities of Posts and Telecommunications》2018年第6期97-100,共4页中国邮电高校学报（英文版）

摘　　要：An optimized Neumann series（NS） approximation is described based on Frobenius matrix decomposition, this method aims to reduce the high complexity, which caused by the large matrix inversion of detection algorithm in the massive multiple input multiple output（MIMO） system. The large matrix in the inversion is decomposed into the sum of the hollow matrix and a Frobenius matrix, and the Frobenius matrix has the diagonal elements and the first column of the large matrix. In order to ensure the detection performance approach to minimum mean square error（MMSE） algorithm, the first three terms of the series approximation are needed, which results in high complexity as O（K;）, where K is the number of users. This paper further optimize the third term of the series approximation to reduce the computational complexity from O（K;） to O（K;）. The computational complexity analysis and simulation results show that the performance of proposed algorithm can approach to MMSE algorithm with low complexity O（K;）.An optimized Neumann series(NS) approximation is described based on Frobenius matrix decomposition, this method aims to reduce the high complexity, which caused by the large matrix inversion of detection algorithm in the massive multiple input multiple output(MIMO) system. The large matrix in the inversion is decomposed into the sum of the hollow matrix and a Frobenius matrix, and the Frobenius matrix has the diagonal elements and the first column of the large matrix. In order to ensure the detection performance approach to minimum mean square error(MMSE) algorithm, the first three terms of the series approximation are needed, which results in high complexity as O(K^3), where K is the number of users. This paper further optimize the third term of the series approximation to reduce the computational complexity from O(K^3) to O(K^2). The computational complexity analysis and simulation results show that the performance of proposed algorithm can approach to MMSE algorithm with low complexity O(K^2).

关 键 词：massive MIMO jacobi iteration zero forcing precoding low complexity weighted two diagonal iteration 

分 类 号：TN[电子电信]