电信科学 ›› 2017, Vol. 33 ›› Issue (9): 1-9.doi: 10.11959/j.issn.1000-0801.2017213

• 研究与开发 •    下一篇

基于消息传递的大规模多用户MIMO低复杂度的检测算法

王琼,叶伟,吉明明   

  1. 重庆邮电大学通信与信息工程学院,重庆 400065
  • 修回日期:2017-06-29 出版日期:2017-09-01 发布日期:2019-04-20
  • 作者简介:王琼(1971-),女,重庆邮电大学通信与信息工程学院正高级工程师、硕士生导师,主要研究方向为移动通信。|叶伟(1992-),男,重庆邮电大学通信与信息工程学院硕士生,主要研究方向为大规模MIMO系统中信号检测技术。|吉明明(1992-),男,重庆邮电大学通信与信息工程学院硕士生,主要研究方向为非正交多址接入技术。

A low complexity detection algorithm for large scale multiuser MIMO based on message passing

Qiong WANG,Wei YE,Mingming JI   

  1. School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Revised:2017-06-29 Online:2017-09-01 Published:2019-04-20

摘要:

针对大规模多用户多输入多输出(MIMO)系统中基站端检测复杂度高的问题,提出了一种低复杂度、基于强制收敛的变量节点全信息高斯消息传播迭代检测(VFI-GMPID-FC)算法。首先对传统的 GMPID算法进行改进,得到VFI-GMPID算法,VFI-GMPID算法的检测性能逼近最小均方误差检测(MMSE)算法,但复杂度要大大低于MMSE算法。然后结合强制收敛思想和VFI-GMPID,提出VFI-GMPID-FC算法,进一步降低算法复杂度,提升检测效率。最后通过仿真结果表明,所提算法在保证检测性能的同时,能有效地降低算法的复杂度。

关键词: 大规模多用户MIMO, 高斯消息传递迭代检测, 强制收敛, 低复杂度

Abstract:

According to the problem of high complexity of base station detection in large scale multiuser multiple input multiple output (MIMO) system,a low complexity multiuser variable node full information Gaussian message passing iterative detection algorithm based on forced convergence (VFI-GMPID-FC) was proposed.Firstly,the traditional Gaussian message passing iterative detection (GMPID) algorithm was improved to obtain VFI-GMPID algorithm,the detection performance of the VFI-GMPID algorithm approximates the minimum mean square error detection (MMSE) algorithm,but the complexity was considerably less than the MMSE algorithm.Then,the VFI-GMPID-FC algorithm was proposed to reduce the complexity of the algorithm and improve the detection efficiency.Finally,the simulation results show that the proposed algorithm can effectively reduce the algorithm complexity while ensuring the detection performance.

Key words: large scale multiuser MIMO, Gaussian message passing iterative detection, forced convergence, low complexity

中图分类号: 

No Suggested Reading articles found!