北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2016, Vol. 39 ›› Issue (6): 57-61.doi: 10.13190/j.jbupt.2016.06.011

• 论文 • 上一篇    下一篇

Massive MIMO网络中低复杂度的MMSE检测方法

汪彬1, 常永宇1, 权威2, 张戬2   

  1. 1. 北京邮电大学 信息与通信工程学院, 北京 100876;
    2. 华为技术有限公司 北京研究所, 北京 100085
  • 收稿日期:2016-01-15 出版日期:2016-12-28 发布日期:2016-11-29
  • 作者简介:汪彬(1989-),男,博士生,E-mail:wangbin1012@126.com;常永宇(1963-),女,教授,博士生导师.
  • 基金资助:
    北京邮电大学优秀博士创新基金项目(CX2015204)

Low-Complexity MMSE Detection Methods for Massive MIMO Networks

WANG Bin1, CHANG Yong-yu1, QUAN Wei2, ZHANG Jian2   

  1. 1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. Huawei Technologies Co., Ltd, Beijing 100085, China
  • Received:2016-01-15 Online:2016-12-28 Published:2016-11-29

摘要: 设计出大规模多入多出(massive MIMO)网络中一种有效、低复杂度的最小均方误差(MMSE)接收机结构.该接收机基于最陡下降和最小均方算法,能够避免大规模矩阵求逆运算.数学推导结果证明,这2种算法应用于MMSE检测时是收敛的.此外,分析了影响收敛速度的因素.仿真结果表明,应用于Massive MIMO网络中时该接收机收敛速度较快,切实可行.

关键词: 大规模多入多出, 最小均方误差, 最陡下降, 最小均方

Abstract: An efficient low-complexity minimum mean-squared-error (MMSE) receiver structure for massive multiple-input multiple-output (massive MIMO) networks is proposed. It is based on the steepest descent and least mean-square algorithms which can be devoid of the matrix inverse operation.The two algorithms are proved to be convergent when applied in the MMSE detection.Factors that affect the rate of convergence are also analyzed.Analysis and simulation shows that the proposed receiver architecture enjoys a low complexity property and can provide virtually the same performance as the conventional MMSE receiver.

Key words: massive multiple-input multiple-output, minimum mean-squared-error, steepest descent, least mean-square

中图分类号: