OFDM系统中基于贝叶斯学习的联合稀疏信道估计与数据检测

doi:10.11896/jsjkx.191100090

计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 349-353.doi: 10.11896/jsjkx.191100090

OFDM系统中基于贝叶斯学习的联合稀疏信道估计与数据检测

陈平¹, 郭秋歌², 李攀¹, 崔峰^1,2

1 济源职业技术学院信息工程系河南济源 459000
2 河南黄河河务局信息中心郑州 450001

出版日期:2020-11-15 发布日期:2020-11-17
通讯作者: 郭秋歌(zzugqg@163.com)
作者简介:1169904263@qq.com
基金资助:
河南省高新技术领域科技攻关项目(172102210606);河南省高等学校重点科研项目(16B520018);济源市科技攻关项目(16022016)

Joint Sparse Channel Estimation and Data Detection Based on Bayesian Learning in OFDM System

CHEN Ping¹, GUO Qiu-ge², LI Pan¹, CUI Feng^1,2

1 Department of Information Engineering,Jiyuan Vocational andTechnical College,Jiyuan,Henan 459000,China
2 Information Center of Henan Yellow River Bureau,Zhengzhou 450001,China

Online:2020-11-15 Published:2020-11-17
About author:CHEN Ping,born in 1982,master,lecturer.His main research include network communication application technology,embedded application technology and signal processing.
GUO Qiu-ge,born in 1992,master,engineer.His main research include water conservancy informatization,signal processing and image processing.
Supported by:
This work was supported by the Key Scientific and Technological Projects in the Field of High and New Technology in Henan Province(172102210606),Key Scientific Research Projects of Universities in Henan Province(16B520018)and Jiyuan Science and Technology Project(16022016).

摘要/Abstract

摘要： 众所周知,一个宽带无线信道的冲击响应是近似稀疏的,从某种意义上,相对于时延扩展来讲,它仅有一小部分重要的组成成分。针对正交频分复用系统,基于稀疏贝叶斯学习方法,提出两种稀疏信道估计算法:稀疏贝叶斯算法和联合稀疏贝叶斯算法。在信道测量矩阵未知的情况下,所提算法仍能够有效地估计出信道抽头。蒙特卡洛仿真显示,与经典正交匹配追踪算法和变分消息传递算法相比,所提算法在均方误差和误码率相同的情况下,信噪比有3～5 dB的提升。

关键词: 变分消息传递, 近似稀疏, 稀疏贝叶斯学习, 信道估计, 正交匹配追踪, 正交频分复用

Abstract: It is well known that the impulse response of a wide band wireless channel is approximately sparse,in the sense that it has a small number of significant components relative to the channel delay spread.In this paper,two sparse channel estimation algorithms based on spare bayesian learning (SBL) method are proposed for orthogonal frequency division multiplexing (OFDM) system,which we call SBL algorithm and J-SBL algorithm.In the case of unknown channel measurement matrix,the proposed algorithms can still estimate channel taps effectively.Compared with the classical algorithms:orthogonal matching pursuing (OMP) algorithm and variational messaging(VMP) algorithm,montecarlo simulation shows that the proposed algorithms perform better than classical algorithms in terms of the same mean square error and bit error rate and their SNR is improved by 3～5 dB.

Key words: A-sparse, Channel estimation, Orthogonal frequency division multiplexing, Orthogonal matching pursuit, Sparse Baye-sian learning, Variational message passing

中图分类号:

TP911

陈平, 郭秋歌, 李攀, 崔峰. OFDM系统中基于贝叶斯学习的联合稀疏信道估计与数据检测[J]. 计算机科学, 2020, 47(11A): 349-353. https://doi.org/10.11896/jsjkx.191100090

CHEN Ping, GUO Qiu-ge, LI Pan, CUI Feng. Joint Sparse Channel Estimation and Data Detection Based on Bayesian Learning in OFDM System[J]. Computer Science, 2020, 47(11A): 349-353. https://doi.org/10.11896/jsjkx.191100090

参考文献

[1] PEDERSEN N L,MANCHÓN C N,SHUTIN D,et al.Fleury Appli-cation of Bayesian hierarchical prior modeling to sparse channel estimation[C]//Proc.ICC.Ottawa,ON,Canada,2015:3487-3492.
[2] ZHANG X,XIAO P,MA D,et al.Variational Bayes assisted joint signal detection,noise covariance estimation and channel tracking in MIMO-OFDM systems[J].IEEE Trans.Veh.Technol.,2014,PP(99):1-1.
[3] HWANG S J,SCHNITER P.Efficient multicarrier communication for highly spread underwater acoustic channels[J].IEEE J.Sel.Areas Commun.,2008,26(9):1674-1683.
[4] BAJWA W,HAUPT J,SAYEED A,et al.Compressed channel sensing:A new approach to estimating sparse multipath channels[C]//Proc.IEEE.2010:1058-1076.
[5] SCHNITER P.Belief-propagation-based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels[J].Phys.Commun.,2016,5(2):91-101.
[6] TAUBOCK G,HLAWATSCH F.A compressed sensing technique for OFDM channel estimation in mobile environments:Exploiting channel sparsity for reducing pilots[C]//Proc.ICASSP.Las Vegas,NV,USA,2008:2885-2888.
[7] SHARP M,SCAGLIONE A.Application of sparse signal reco-very to pilot-assisted channel estimation[C]//Proc.ICASSP.Las Vegas,NV,USA,2008:3469-3472.
[8] BERGER C,WANG Z,HUANG J,et al.Application of com-pressive sensing to sparse channel estimation[J].IEEE Commun.Mag.,2013,48(11):164-174.
[9] SCHNITER P.A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels[J].IEEE J.Sel.Topics Signal Process.,2015,5(8):1462-1474.
[10] KANNU A P,SCHNITER P.On communication over unknown sparse frequency-selective block-fading channels[J].IEEE Trans.Inf.Theory,2015,57(10):6619-6632.
[11] WANG Z Y,GUO Q G,WANG F S,et al.Low-complexity sparse channel estimation for SC-FDE system based on hierarchical model[J].Signal Processing,2015,31(9):1106-1111.
[12] YUAN Z D,WANG Z Y,ZHANG C Z,et al.Joint Channel Eestimation Receiver based on Stretched Factor Graph for SCFDE System[J].Electronics Applications,2017,43(8):110-114.
[13] PRASAD R,MURTHY C.Bayesian learning for joint sparseOFDM channel estimation and data detection[C]//Proc.GLOBECOM.Miami,FL,USA,2010:1-6.
[14] ZINIEL J,POTTER L C,SCHNITER P.Tracking and smoothing of time-varying sparse signals via approximate belief propagation[C]//Proc. Asilomar Conf.Signals,Syst.,Comput..Pacific Grove,CA,USA,2014:808-812.
[15] ZHANG Z,RAO B.Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J].IEEE J.Sel.Topics Signal Process.,2011,5(5):912-926.
[16] ZINIEL J,SCHNITER P.Efficient high-dimensional inference in the multiple measurement vector problem[J].IEEE J.Sel.Topi-cs Signal Process.,2013,61(2):340-354.
[17] VASWANI N.Kalman filtered compressed sensing[C]//Proc.ICIP.San Diego,CA,USA,2008:893-896.
[18] WIPF D P,RAO B D.Sparse Bayesian learning for basis selection[J].IEEE Trans.Signal Process.,2004,52(8):2153-2164.
[19] PEDERSEN N L,SHUTIN D,MANCHON C N,et al.Sparse estimation using Bayesian hierarchical prior modeling for real and complex models[J].Signal Processing,2015,115(6):94-109.
[20] NAFFOURI T Y A,BAHAI A,PAULRAJ A.An EM-basedOFDM receiver for time-variant channels[C]//Proc.GLOBECOM.2002:589-593.
[21] WU C.On the convergence properties of the EM algorithm[J].Ann.Statist.,1983,11(1):95-103.
[22] MCLACHLAN G,KRISHNAN T.The EM Algorithm and Extensions[M].New York:Wiley,1997:274.

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OFDM系统中基于贝叶斯学习的联合稀疏信道估计与数据检测

Joint Sparse Channel Estimation and Data Detection Based on Bayesian Learning in OFDM System

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