Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> A. FLAT FADING CHANNEL PREDICTION 1) CHANNEL GAIN PREDICTION BY A COMPLEX-VALUED RNN <s> In this contribution, the application of fully connected recurrent neural networks (FCRNNs) is investigated in the context of narrowband channel prediction. Three different algorithms, namely the real time recurrent learning (RTRL), the global extended Kalman filter (GEKF) and the decoupled extended Kalman filter (DEKF) are used for training the recurrent neural network (RNN) based channel predictor. Our simulation results show that the GEKF and DEKF training schemes have the potential of converging faster than the RTRL training scheme as well as attaining a better MSE performance. <s> BIB001 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> A. FLAT FADING CHANNEL PREDICTION 1) CHANNEL GAIN PREDICTION BY A COMPLEX-VALUED RNN <s> 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. <s> BIB002
To begin with, consider a discrete-time baseband equivalent model for a flat fading SISO channel: The aim of RNN predictor is to get a predicted valueĥ[t+τ ] that is as close as possible to its actual value h[t+τ ]. To deal with complex-valued channel gains, a network with complex-valued weights called a complex-valued RNN hereinafter is needed BIB001 , BIB002 .