电子学报 ›› 2019, Vol. 47 ›› Issue (12): 2515-2523.DOI: 10.3969/j.issn.0372-2112.2019.12.009

• 学术论文 • 上一篇    下一篇

基于隐聚类和狄利特雷过程的大规模MIMO-OFDM接收机设计

崔建华1, 袁正道2,3, 王忠勇3, 路新华3, 薛琦3   

  1. 1. 洛阳师范学院物理与电子信息学院, 河南洛阳 471934;
    2. 河南广播电视大学博士后工作站, 河南郑州 450001;
    3. 郑州大学信息工程学院, 河南郑州, 450001
  • 收稿日期:2018-09-14 修回日期:2019-05-13 出版日期:2019-12-25
    • 作者简介:
    • 崔建华 女,1981年出生,河南原阳人,博士,副教授,主要研究方向:通信信号处理、无线传感器网络.;袁正道 男,1983年出生,河南郑州人,博士后,讲师,主要研究方向:大规模天线系统、迭代信号处理、无线传感器网络.;王忠勇 男,1965年出生,江西吉安人,博士,教授,博士生导师,主要研究方向:通信信号处理、嵌入式系统设计.;路新华 男,1980年出生,山东济南人,博士研究生,主要研究方向:大规模天线系统、通信信号处理、机器学习.;薛琦 男,1982年出生,河南濮阳人,博士,讲师,主要研究方向:信号处理、光电测量.
    • 基金资助:
    • 国家自然科学基金面上项目 (No.61571402); 国家青年科学基金 (No.61705198); 博士后科学基金 (No.2019M652576),河南省科技攻关项目 (No.182102210573); 河南省教育厅高校重点研究项目 (No.19A510019)

Massive MIMO-OFDM Receiver Design Based on Hidden Cluster Hypothesis and Dirichlet Process

CUI Jian-hua1, YUAN Zheng-dao2,3, WANG Zhong-yong3, LU Xin-hua3, XUE Qi3   

  1. 1. School of Physics and Electronic Information, Luoyang Normal University, Luoyang, Henan 471934, China;
    2. Postdoctoral Workstation, Henan TV & Radio University, Zhengzhou, Henan, 450001;
    3. School of Information Engineering, Zhengzhou University, Zhengzhou, Henan 450001, China
  • Received:2018-09-14 Revised:2019-05-13 Online:2019-12-25 Published:2019-12-25
    • Supported by:
    • National Natural Science Foundation of China (No.61571402); Youth Fund of National Natural Science Foundation of China (No.61705198); China Postdoctoral Science Foundation (No.2019M652576); Science and Technology Research and Development Program of Henan Province (No.182102210573); Key Research Project of Higher Education Department of Henan Province (No.19A510019)

摘要: 本文首先讨论了大规模MIMO-OFDM(Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing)系统信道的空间相关性,提出了一种基于隐聚类假设的信道建模方法,利用概率参数模拟不同的传播环境.然后,将机器学习领域的狄利特雷过程(Dirichlet Process,DP)引入到稀疏贝叶斯学习(Sparse Bayesian Learning,SBL)模型中,建立了DP-SBL结构,在信道估计的同时挖掘并利用大规模MIMO系统所特有的隐聚类特征.接着,将DP-SBL结构应用于大规模MIMO-OFDM系统中,在因子图上利用消息传递算法推导了一种基于隐聚类和狄利特雷过程的接收机算法.最后,将本文提出的接收机算法和现有算法进行对比分析.结果表明,本文提出的接收机算法充分利用了大规模MIMO-OFDM系统特有的空间相关性,能够以较低的计算复杂度获得较强的鲁棒性和显著的性能增益.

关键词: 大规模MIMO, 迭代接收机设计, 隐聚类假设, 狄利特雷过程, 消息传递算法

Abstract: The paper discusses the spatial correlation of channels in massive MIMO-OFDM system, and proposes a hidden clustering hypothesis to simulations different propagation environments with probability parameters. Then, the Dirichlet process (DP) in machine learning is introduced into sparse Bayesian learning (SBL) model and a DP-SBL structure is established. Consequently, the hidden clustering features of massive MIMO system are explored simultaneously in the process of channel estimation. Furthermore,the DP-SBL structure is applied to massive MIMO-OFDM systems, and a receiver algorithm based on hidden clustering and Dirichlet process is deduced by using message passing algorithm on factor graphs. Finally, we compare the proposed algorithm with the existing algorithms. Simulation results show that the proposed algorithm can exploit and utilize the spatial resources of massive MIMO-OFDM system. It can achieve remarkable performance gain with low computation complexity and strong robustness.

Key words: massive MIMO, iterative receiver design, hidden cluster hypothesis, Dirichlet process, message passing algorithm

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