Nonlinear model based prediction of time varying SISO-MIMO channels using FANN-DFE combination

In this paper, we present a method for real-time identification and prediction of time varying mobile radio channel. The predictor comprises of two main units. First, a Feed Forward Artificial Neural Network (FANN) is used to identify the auto regressive (AR) system underlying the fading mechanism. Estimated AR model is then used to predict the future state of the channel. The so obtained channel state information (CSI) is used to equalize the received symbol when it arrives. Since training symbols used are minimal in the prediction state, a Decision Feedback Equalization (DFE) type of architecture is proposed to aid the correction step. The model identification is done for channel gains generated by Jack's model. Experimental results show that the coupled predictor performs better compared to the model or the DFE alone in time varying SISO-MIMO channels.