基于隐聚类和狄利特雷过程的大规模MIMO-OFDM接收机设计

doi:10.3969/j.issn.0372-2112.2019.12.009

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

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. 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-dao^2,3, WANG Zhong-yong³, LU Xin-hua³, XUE Qi³

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/Abstract

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

中图分类号:

TN911.23

崔建华, 袁正道, 王忠勇, 等. 基于隐聚类和狄利特雷过程的大规模MIMO-OFDM接收机设计[J]. 电子学报, 2019, 47(12): 2515-2523.

CUI Jian-hua, YUAN Zheng-dao, WANG Zhong-yong, et al. Massive MIMO-OFDM Receiver Design Based on Hidden Cluster Hypothesis and Dirichlet Process[J]. Acta Electronica Sinica, 2019, 47(12): 2515-2523.

参考文献

[1] Larsson E G,Edfors O,Tufvesson F,et al.Massive MIMO for next generation wireless systems[J].IEEE Communications Magazine,2014,52(2):186-195.
[2] 罗仁泽,杨娇,李芮,等.MIMO-OFDM系统训练序列构造及其同步方法[J].电子学报,2014,42(9):1781-1785. Luo Ren-ze,Yang Jiao,Li Rui,et al.The construct of training sequence and synchronization for MIMO-OFDM system[J].Acta Electronica Sinica,2014,42(9):1781-1785.(in Chinese)
[3] Narasimhan T L,Chockalingam A.Channelhardening-exploiting message passing (CHEMP) receiver in large-scale MIMO systems[J].IEEE Journal of Selected Topics in Signal Processing,2014,8(5):847-860.
[4] Björnson E,Hoydis J,Kountouris M,et al.Massive MIMOsystems with non-ideal hardware:energy efficiency,estimation,and capacity limits[J].IEEE Transactions on Information Theory,2014,60(11):7112-7139.
[5] Bajwa W U,Haupt J,Sayeed A M,et al.Compressed channel sensing:a new approach to estimating sparse multipath channels[J].Proceedings of the IEEE,2010,98(6):1058-1076.
[6] Yuan Z,Zhang C,Guo Q,et al.Combined message passing based SBI with dirichlet process prior for sparse signal recovery with multiple measurement vectors[J].IEEE Access,2018,6(2):13181-13190.
[7] 冯子奇,牛勇,苏厉,等.基于混合波束赋形的室内毫米波MIMO系统性能分析[J].电子学报,2017(6):1281-1287. Feng Zi-qi,Niu Yong,Su Li,et al.Performance analysis of the hybrid beamforming based indoor millimeter wave MIMO system[J].Acta Electronica Sinica,2017,45(6):1281-1287.(in Chinese)
[8] Schniter P.Amessage-passing receiver for BICM-OFDM over unknown clustered-sparse channels[J].IEEE Journal of Selected Topics in Signal Processing,2011,5(8):1462-1474.
[9] Prasad R,Murthy C R,Rao B D.Joint channel estimation and data detection in MIMO-OFDM systems:a sparse bayesian learning approach[J].IEEE Transactions on Signal Processing,2015,63(20):5369-5382.
[10] Barbotin Y,Hormati A,Rangan S,et al.Estimation ofsparse MIMO channels with common support[J].IEEE Transactions on Communications,2012,60(12):3705-3716.
[11] Nan Y,Zhang L,Sun X.Efficientdownlink channel estimation scheme based on block-structured compressive sensing for TDD massive MU-MIMO systems[J].IEEE Wireless Communications Letters,2015,4(4):345-348.
[12] Masood M,Afify L H,Al-Naffouri T Y.efficient coordinated recovery of sparse channels in massive MIMO[J].IEEE Transactions on Signal Processing,2015,63(1):104-118.
[13] Lin X,Wu S,Kuang L,et al.Estimation of sparse massive MIMO-OFDM channels with approximately common support[J].IEEE Communications Letters,2017,21(5):1179-1182.
[14] Ferguson T S.Priordistributions on spaces of probability measures[J].Annals of Statistics,1974,2(4):615-629.
[15] 周建英,王飞跃,曾大军.分层Dirichlet过程及其应用综述[J].自动化学报,2011,37(4):389-407. Zhou Jian-ying,Wang Fei-yue,Zeng Da-jun,Hierarchical Dirichlet processes and their applications:a survey[J].Acta Automatica Sinica,2011,37(4):389-407.(in Chinese)
[16] 张凯,于宏毅,胡赟鹏,等.基于EM-SBL迭代的稀疏SIMO信道频域盲均衡算法[J].电子学报,2018,46(2):372-380. Zhang Kai,Yu Hong-yi,Hu Yun-peng,et al.Blind frequency-domain equalization for sparse SIMO channels based on iterative EM-SBL algorithm[J].Acta Electronica Sinica,2018,46(2):372-380.(in Chinese)
[17] Wang L,Zhao L,Bi G,et al.Novelwideband doa estimation based on sparse bayesian learning with dirichlet process priors[J].IEEE Transactions on Signal Processing,2016,64(2):275-289.
[18] Yuan Z,Zhang C,Wang Z,et al.Anauxiliary variable-aided hybrid message passing approach to joint channel estimation and decoding for MIMO-OFDM[J].IEEE Signal Processing Letters,2017,24(1):12-16.
[19] Kirkelund G E,Manchon C N,Christensen L P B,et al.Variationalmessage-passing for joint channel estimation and decoding in MIMO-OFDM[A].2010 IEEE Global Telecommunications Conference (GLOBECOM 2010)[C].Miami,FL,USA:IEEE, 2010.1-6.
[20] Rangan S.Generalized approximate message passing for estimation with random linear mixing[A].2011 IEEE International Symposium on Information Theory Proceedings[C].St Petersburg,Russia:IEEE,2011.2168-2172.
[21] Zhang C,Yuan Z,Wang Z,et al.Low complexity sparse Bayesian learning using combined belief propagation and mean field with a stretched factor graph[J].Signal Processing,2017,131:344-349.
[22] Wu S,Ni Z,Meng X,et al.Blockexpectation propagation for downlink channel estimation in massive MIMO systems[J].IEEE Communi-cations Letters,2016,20(11):2225-2228.

基于隐聚类和狄利特雷过程的大规模MIMO-OFDM接收机设计

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

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