计算机与现代化 ›› 2024, Vol. 0 ›› Issue (04): 1-4.doi: 10.3969/j.issn.1006-2475.2024.04.001

• 算法设计与分析 •    下一篇

一种基于联合加权和截断的毫米波大规模MIMO信道估计

  



  1. (南京邮电大学通信与信息工程学院,江苏 南京 210003)
  • 出版日期:2024-04-30 发布日期:2024-05-13
  • 作者简介: 作者简介:张志能(1996—),男,江苏淮安人,硕士研究生,研究方向:无线通信系统,E-mail: 2631253783@qq.com; 黄学军(1967—),男,安徽天长人,副教授,博士,研究方向:现代无线通信,物联网,E-mail: huangxj@njupt.edu.cn。
  • 基金资助:
      基金项目:国家自然科学基金资助项目(61427801)

A mmWave Massive MIMO Channel Estimation Based on Joint Weighted#br# and Truncated Nuclear Norm

  1. (School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, 
    Nanjing 210003, China)
  • Online:2024-04-30 Published:2024-05-13

摘要: 摘要:提出一种联合加权和截断核范数的毫米波大规模多输入多输出(MIMO)信道估计算法。针对毫米波大规模MIMO信道估计问题中训练和反馈开销大的问题,首先利用毫米波信道天线角度域稀疏的特性,把信道估计问题转化为低秩矩阵恢复问题。采用一种有效而灵活的秩函数——联合加权截断核范数作为核范数的松弛,构造出一种新的矩阵恢复模型用于信道估计问题,以最小化加权截断核范数为优化目标,并利用交替优化框架求解。仿真结果表明,该方法可以有效地提高信道估计的精度,并且具有可靠的收敛性。




关键词: 关键词:低秩矩阵恢复, 毫米波大规模MIMO, 信道估计, 截断核范数

Abstract: Abstract: In this paper, a millimeter-wave massive multiple input multiple output (MIMO) channel estimation algorithm based on joint weighted and truncated nuclear norm is proposed. Aiming at the problem of high training and feedback overhead in millimeter-wave massive MIMO channel estimation, firstly, the channel estimation problem is transformed into a low-rank matrix recovery problem by using the sparse antenna angle domain of millimeter-wave channel. An effective and flexible rank function, the joint weighted and truncated kernel norm, is adopted as the relaxation of the nuclear norm, and a new matrix recovery model is constructed for channel estimation. The optimization objective is to minimize the weighted and truncated nuclear norm, and it is solved by an alternating optimization framework. The simulation results show that this method can effectively improve the accuracy of channel estimation and has reliable convergence.

Key words: Key words: low rank matrix recovery, millimeter wave massive MIMO, channel estimation, truncated nuclear norm

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