JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2018, Vol. 25 ›› Issue (6): 97-100.doi: 10.19682/j.cnki.1005-8885.2018.1032

• Wireless • Previous Articles    

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

Qu Tuosi, Cao Haiyan, Xu Fangmin, Wang Xiumin   

  1. 1. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    2. College of Information Engineering, China Jiliang University, Hangzhou 310018, China
  • Received:2018-04-23 Revised:2018-12-27 Online:2018-12-30 Published:2019-02-26
  • Contact: Qu Tuosi, E-mail:
  • About author:Qu Tuosi, E-mail:

Abstract: 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(K3), 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(K3) to O(K2). The computational complexity analysis and simulation results show that the performance of proposed algorithm can approach to MMSE algorithm with low complexity O(K2).

Key words: massive MIMO, jacobi iteration, zero forcing precoding, low complexity, weighted two diagonal iteration

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