Channel Quality Prediction in 5G LTE Small Cell Mobile Network Using Deep Learning

Abstract

Prior knowledge of wireless channel quality with high accuracy is essential to enable anticipated networking tasks. Traditional channel quality prediction problems rely on past channel information to predict its future quality. In this paper, we investigate the channel quality prediction problem over different wireless channels. We propose an efficient prediction scheme based on deep learning, to predict channel quality. For the deep learning task, we use deep neural networks and long short-term memory networks. We compare their performance on a dataset collected from a commercial 4G mobile radio network of Orange Senegal. The performance evaluation performed on the benchmark dataset demonstrates the validity of the proposed deep learning approach, reaching a root mean square error of 0.27 for the LSTM model and 0.28 for the DNN model. The performances in terms of RMSE with the same dataset for each of the models used in this study were compared to other models. Thus, the DNN and LSTM models give low RMSEs compared to the models of our previous work. The proposed prediction method can be applied for 5G small cell networks.