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

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.