Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks

The forthcoming fifth generation networks require improvements in the cognitive radio intelligence, going towards more smart and aware radio systems. More in depth, in the emerging radio intelligence approach, the empowerment in cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application ofthe convolutional and the recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, the system performance has been analyzed in terms of prediction accuracy expressed as absolute deviation error and mean percentage error, in comparison with an alternative machine learning method recently proposed in the literature with the aim at solving the same prediction problem.