Advertisement

Multi-path Channel Estimation Using Empirical Likelihood Algorithm with Non-Gaussian Noise

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

Abstract

In this paper a novel algorithm, empirical likelihood, is employed to estimate the channel taps of multi-path channel under Non-Gaussian noise. To illustrate, multi-path channel is modeled as a FIR filter and non-Gaussian noise is taken as mixed Additive White Gaussian and impulse noise for simplicity. Thus the transmitted signal, being impacted by the multi-path fading effect and being disturbed by the non-Gaussian noise, would be sampled to form the received signal. And simulations show that the proposed algorithm is capable of obtaining good MSE and bit error rate BER performances.

Keywords

Multi-path channel estimation Non-Gaussian noise Empirical likelihood 

Notes

Acknowledgement

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).

References

  1. 1.
    Tsatsanis MK, Giannakis GB, Zhou G (1996) Estimation and equalization of fading channels with random coefficients. Signal Process 53:211–229CrossRefMATHGoogle Scholar
  2. 2.
    Crozier SN, Falconer DD, Mahmoud SA (1991) Least sum of squared errors (LSSE) channel estimation. IEE Proc F 138(4):371–378Google Scholar
  3. 3.
    Van Trees HL (1968) Detection, estimation, and modulation theory, Part I. Wiley, LondonMATHGoogle Scholar
  4. 4.
    Davis LM, Collings IB, Evans RL (1997) Identification of time-varying linear channels. Proc. ICASSP’97, Apr 1997, Munich, GermanyGoogle Scholar
  5. 5.
    Owen AB (1988) Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75:237–249CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Owen AB (1988) Small sample central confidence intervals for the mean. Technical report 302, Dept. Statistics, Stanford UnivGoogle Scholar
  7. 7.
    Owen AB (1988) Computing empirical likelihoods. In: Wegman EJ, Gantz PT, Miller JJ (eds) Computing science and statistics. Proceedings of the 20th symposium on the interface. American Statistics Association, Alexandria, VA, pp 442–447Google Scholar
  8. 8.
    Owen AB (1990) Empirical likelihood confidence regions. Ann Stat 18:90–120CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Owen AB (1991) Empirical likelihood for linear models. Ann Stat 19:1725–1747CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Owen AB (2001) Empirical likelihood. Chapman and Hall, New YorkCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pengbiao Wang
    • 1
  • Yan Zhang
    • 1
  • Long Zhao
    • 1
  • Bin Li
    • 1
  • Chenglin Zhao
    • 1
  1. 1.Key Lab of Universal Wireless Communications, MOE Wireless Network LabBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations