Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

With accurate traffic prediction, future cellular networks can make self-management and embrace intelligent and efficient automation. This letter devotes itself to citywide cellular traffic prediction and proposes a deep learning approach to model the nonlinear dynamics of wireless traffic. By treating traffic data as images, both the spatial and temporal dependence of cell traffic are well captured utilizing densely connected convolutional neural networks. A parametric matrix based fusion scheme is further put forward to learn influence degrees of the spatial and temporal dependence. Experimental results show that the prediction performance in terms of root mean square error can be significantly improved compared with those existing algorithms. The prediction accuracy is also validated by using the data sets of Telecom Italia.