A deep learning method based on an attention mechanism for wireless network traffic prediction

With the rapid development of wireless networks, the self-management and active adjustment capabilities of base stations have become crucial. The accurate prediction of wireless network traffic is an important prerequisite for intelligent base stations. Traffic data has a high degree of nonlinearity and complexity, which is characterized by temporal and spatial correlation. Most of the existing forecasting methods do not consider both the temporal and spatial situations in the process of modeling traffic data. In this paper, a spatio-temporal convolutional network (LA-ResNet) is presented that uses an attention mechanism to solve spatio-temporal modeling and predict wireless network traffic. LA-ResNet consists of three parts: the residual network, the recurrent neural network, and an attention mechanism. Using this method, the temporal and spatial characteristics of wireless network traffic data are modeled and its related features are strengthened. Thus, the spatio-temporal correlation of wireless network traffic data can be effectively captured. The residual network can capture spatial features in the data. The combination of the recurrent neural network and the attention mechanism can capture the temporal dependence of the data. Finally, experiments on a real data set show that the prediction effect of the LA-ResNet model is better than the other existing prediction methods, such as RNN and 3DCNN, and the accurate prediction of traffic can be realized. (C) 2020 Elsevier B.V. All rights reserved.