Fading Channel Prediction Based on Combination of Complex-Valued Neural Networks and Chirp Z-Transform | IEEE Journals & Magazine | IEEE Xplore

Fading Channel Prediction Based on Combination of Complex-Valued Neural Networks and Chirp Z-Transform


Abstract:

Channel prediction is an important process for channel compensation in a fading environment. If a future channel characteristic is predicted, adaptive techniques, such as...Show More

Abstract:

Channel prediction is an important process for channel compensation in a fading environment. If a future channel characteristic is predicted, adaptive techniques, such as pre-equalization and transmission power control, are applicable before transmission in order to avoid degradation of communications quality. Previously, we proposed channel prediction methods employing the chirp z-transform (CZT) with a linear extrapolation as well as a Lagrange extrapolation of frequency-domain parameters. This paper presents a highly accurate method for predicting time-varying channels by combining a multilayer complex-valued neural network (CVNN) with the CZT. We demonstrate that the channel prediction accuracy of the proposed CVNN-based prediction is better than those of the conventional prediction methods in a series of simulations and experiments.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 25, Issue: 9, September 2014)
Page(s): 1686 - 1695
Date of Publication: 04 March 2014

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