Neural Network Channel Estimation Based on Least Mean Error Algorithm in the OFDM Systems
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.
KeywordsChannel Estimation Pilot Symbol Doppler Power OFDM System Frequency Channel Response
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- 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
- 7.Proakis, J.G.: Digital Communications, 4th edn. McGraw-Hill, New York (2001)Google Scholar