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. 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
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.
[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.
2019, Vol. 47 
