A Dynamic Bernstein Graph Recurrent Network for Wireless Cellular Traffic Prediction

Predictive analysis of wireless cellular traffic plays an important role in network resources provisioning. Accurate traffic prediction is a challenging task due to the dynamic spatial-temporal nature of wireless traffic. Most of the existing approaches do not consider spectral domain information for wireless traffic prediction. Some of the approaches cannot capture the spatial dependencies between neighbouring and distant cells. In this paper, we propose a dynamic Bernstein graph recurrent network for traffic prediction in wireless cellular networks. First, we design a spectral dynamic graph construction (SDGC) method to model the spatial dependencies between cells as a dependency graph in a data-driven fashion. A dynamic Bernstein polynomial filtering (DBPF) scheme based on the
$K$
-order Bernstein polynomial approximation is then developed to capture the spatial correlations and infer the cell-specific parameters. To predict the spatial-temporal traffic demands, we propose a dynamic Bernstein graph recurrent network (DBGRN), which integrates the proposed DBPF module with a gated recurrent unit (GRU) network. We evaluate the performance of our proposed model using a real-world dataset. Results show that our proposed model outperforms four state-of-the-art baseline schemes, and achieves up to 8% and 10% improvements in terms of the root mean squared error (RMSE) and mean absolute error (MAE), respectively.