Advertisement

Neural Network Channel Estimation Based on Least Mean Error Algorithm in the OFDM Systems

  • Jun Sun
  • Dong-Feng Yuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

We designed a new channel estimator including two parts of neural network to estimate the amplitude and the angle of the frequency domain channel coefficients, respectively. The least mean error (LSE) is used for training. This neural network channel estimator (NNCE) makes full use of the learning property of the neural network (NN). Once the NN was trained, it reflected the channel fading trait of the amplitude and the angle respectively. It was no need of any matrix computation and it can get any required accuracy. It has been validated that the estimator is available in the pilot-symbol-aided (PSA) OFDM system.

Keywords

Channel Estimation Pilot Symbol Doppler Power OFDM System Frequency Channel Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mignone, V., Morello, A.: CD3-OFDM: A Novel Demodulation Scheme for Fixed and Mobile Receivers. IEEE Transactions on Communications 44(9), 1144–1151 (1996)CrossRefGoogle Scholar
  2. 2.
    Hoeher, P., Kaiser, S., Robertson, P.: Two-dimensional Pilot-Symbol-Aided Channel Estimation by Wiener Filtering. In: IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. 3, pp. 1845–1848. IEEE, Munich (1997)Google Scholar
  3. 3.
    Hoeher, X.P., Kaiser, S., Robertson, P.: Pilot-symbol-aided Channel Estimation in Time and Frequency. In: IEEE Global Telecommunication Conf., Phoenix, vol. 1, pp. 90–96 (1997)Google Scholar
  4. 4.
    Li, Y.: Pilot-symbol-aided Channel Estimation for OFDM in Wireless Systems. IEEE Transactions on Vehicular Technology 49(7), 1207–1215 (2000)CrossRefGoogle Scholar
  5. 5.
    Ibnkahla, M., Yuan, J.: A Neural Network MLSE Receiver Based on Natural Gradient Descent: Application to Satellite Communications. In: Seventh International Symposium on Signal Processing and Its Applications Proceedings, vol. 1, pp. 33–36. IEEE, Paris (2003)CrossRefGoogle Scholar
  6. 6.
    Pätzold, M.: Mobile Fading Channels. John Wiley & Sons, England (2002)CrossRefGoogle Scholar
  7. 7.
    Proakis, J.G.: Digital Communications, 4th edn. McGraw-Hill, New York (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Sun
    • 1
  • Dong-Feng Yuan
    • 1
    • 2
  1. 1.School of Information Science and EngineeringShandong UniversityJinanP.R. China
  2. 2.State Key Lab. on Mobile CommunicationsSoutheast UniversityNanjingP.R. China

Personalised recommendations