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

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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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.

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References

  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)

    Article  Google Scholar 

  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. 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. Li, Y.: Pilot-symbol-aided Channel Estimation for OFDM in Wireless Systems. IEEE Transactions on Vehicular Technology 49(7), 1207–1215 (2000)

    Article  Google Scholar 

  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)

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  6. Pätzold, M.: Mobile Fading Channels. John Wiley & Sons, England (2002)

    Book  Google Scholar 

  7. Proakis, J.G.: Digital Communications, 4th edn. McGraw-Hill, New York (2001)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Sun, J., Yuan, DF. (2006). Neural Network Channel Estimation Based on Least Mean Error Algorithm in the OFDM Systems. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_104

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  • DOI: https://doi.org/10.1007/11760023_104

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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