Empirical Likelihood-Based Channel Estimation with Laplacian Noise

  • Long Zhao
  • Qiang Ma
  • Bin Li
  • Chenglin Zhao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


This paper introduces a new method to estimate channel with Laplacian noise based on empirical likelihood algorithm. The received signal is assumed to be a transmitted signal which has been corrupted by a multipath channel, modeled as a FIR filter, the output being further disturbed by additive independent Laplacian noise. Then the channel estimation is treated as a nonparametric estimation issue in the model and the channel parameter is estimated by Empirical Likelihood approach. Furthermore, the MSE and BER performance of channel estimation are explored via numerical simulations.


Laplacian noise Empirical likelihood Channel estimation 



This work was supported by the National Natural Science Foundation of China (61271180), Major National Science and Technology Projects (2012zx03001022) and Special Foundation for State Internet of Things Program (Radio frequency and communication security testing service platform of Internet of things).


  1. 1.
    Proakis JG, Dimitris GM (1995) Digital communications, vol 3. McGraw-Hill, New YorkGoogle Scholar
  2. 2.
    Van Trees HL (2004) Detection, estimation, and modulation theory. Wiley, New YorkGoogle Scholar
  3. 3.
    Kassam SA (1989) Signal detection in non-Gaussian noise. Sringer, BerlinGoogle Scholar
  4. 4.
    Marks RJ et al (1978) Detection in Laplace noise. Aerospace Electron Syst IEEE Trans 6:866–872CrossRefGoogle Scholar
  5. 5.
    Beaulieu NC, Niranjayan S (2010) UWB receiver designs based on a Gaussian-Laplacian noise-plus-MAI model. Commun IEEE Trans 58(3):997–1006CrossRefGoogle Scholar
  6. 6.
    Woods JW, O’Neil S (1986) Subband coding of images. Acoust Speech Signal Process IEEE Trans 34(5):1278–1288CrossRefGoogle Scholar
  7. 7.
    Bernstein SL et al (1974) Long-range communications at extremely low frequencies. Proc IEEE 62(3):292–312CrossRefGoogle Scholar
  8. 8.
    Owen AB (1988) Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75(2):237–249CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Owen A (1990) Empirical likelihood ratio confidence regions. Ann Stat 18(1):90–120CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Owen A (1991) Empirical likelihood for linear models. Ann Stat 19(4):1725–1747CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Xu F (2009) An empirical likelihood scheme for signal detection in MIMO systems with nonlinear interference. Circuit Commun Syst. PACCS’09. Pacific-Asia conference on IEEE, 2009, pp 540–543Google Scholar
  12. 12.
    Xu F, Xu X, Zhang P (2007) Semiparametric theory based MIMO model and performance analysis. J China Univ Posts Telecomm 14(4):36–40Google Scholar
  13. 13.
    Li G, Qi-Hua W (2003) Empirical likelihood regression analysis for right censored data. Stat Sinica 13(1):51–68MATHGoogle Scholar
  14. 14.
    Wang Q, Jonnagadda NKR (2002) Empirical likelihood-based inference in linear errors-in-covariables models with validation data. Biometrika 89(2):345–358CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Key Lab of Universal Wireless CommunicationsBeijing University of Posts and TelecommunicationsBeijingChina

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