Neural network channel estimation based on least mean error algorithm in the OFDM systems

Authors:
Jun Sun;Dong-Feng Yuan
Affiliations:
School of Information Science and Engineering, Shandong University, Jinan, P.R. China;School of Information Science and Engineering, Shandong University, Jinan, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 1
Cited 1

Two-Dimensional Pilot-Symbol-Aided Channel Estimation by Wiener Filtering

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3

Channel estimation based on neural network in space time block coded MIMO-OFDM system

Digital Signal Processing

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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.