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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© 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
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