Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

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Abstract: This paper focuses on advancing citywide cellular traffic prediction for enhancing the self-management and intelligent automation of future cellular networks. The proposed approach employs deep learning to model the complex dynamics of wireless traffic. By representing traffic data as images, the method effectively captures both spatial and temporal dependencies in cell traffic using densely connected convolutional neural networks. To refine this model, a parametric matrix-based fusion scheme is introduced, enabling the learning of influence degrees associated with spatial and temporal dependencies. Experimental results demonstrate a substantial improvement in prediction performance, measured by root mean square error (RMSE), compared to existing algorithms. The accuracy of predictions is further validated using datasets from Telecom Italia