Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 349-353.doi: 10.11896/jsjkx.191100090

• Computer Network • Previous Articles     Next Articles

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

CHEN Ping1, GUO Qiu-ge2, LI Pan1, CUI Feng1,2   

  1. 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).

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

CLC Number: 

  • TP911
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