Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

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Abstract This paper addresses the challenges of exponentially growing traffic in cellular networks by proposing a novel predictive model, HGCRN, which combines static graph convolutional recurrent neural network and meta-graph learning. The model is designed to effectively capture the complex spatio-temporal dependencies in network traffic, enhancing prediction accuracy and operational efficiency. By constructing graph adjacency matrices that go beyond mere geographical proximity, HGCRN offers a deeper understanding of the dynamic interactions within the network. Tested on real-world datasets from Telecom Italia and China Mobile, the model demonstrates significant improvements over traditional and state-of-the-art methods in terms of predictive accuracy and reliability.