Long Term 5G Base Station Traffic Prediction Method Based on Spatial-Temporal Correlations

Abstract

In the domain of 5G network management, accurately predicting traffic volumes at base stations remains a critical yet challenging endeavor, primarily due to the complexities inherent in the spatial and temporal data interactions. Current methods often fall short in effectively harnessing long-term trends and spatial interconnections among base stations. To bridge these gaps, this paper introduces the GCformer model, a novel approach that capitalizes on both spatial relationships and temporal patterns for multi-base station traffic prediction. Spatially, the proposed model employs graph convolutional networks to integrate diverse spatial information and construct insightful adjacency matrices that includes Euclidean distances and non-Euclidean distances (area types of base station locations and similarities in traffic flow among various stations), thereby enhancing the predictability of traffic dynamics. Temporally, the application of the Transformer's attention mechanism enables better capture of long-term relational dependencies in the temporal domain of 5G base station traffic data. Additionally, a time-variant optimization module is designed to establish diurnal cycle data for each base station's traffic, replacing the traditional positional encoding with a more nuanced model that improves the learning of historical data correlations. Empirical results from exhaustive case studies confirm the superiority of the GCformer model in forecasting traffic volumes. The GCformer exhibits a 4.01% improvement in mean squared error and a 3.37% enhancement in mean absolute error compared to the best-performing baseline model, showcasing its potential to significantly improve operational strategies in 5G networks.