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Organizational Impact of Spatiotemporal Graph Convolution Networks for Mobile Communication Traffic Forecasting
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Author(s): Pan Ruifeng (Nanchang Vocational University, China), Mengsheng Wang (Nanchang Vocational University, China), Jindan Zhang (Xianyang Polytechnic Institute, China), Brij Gupta (Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan & Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India, & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India, & University of Economics and Human Science, Warsaw, Poland)and Nadia Nedjah (Department of Electronics Engineering and Telecommunications, Brazil)
Copyright: 2025
Volume: 21
Issue: 1
Pages: 19
Source title:
International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.368563
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Abstract
Communication traffic prediction is of great guiding significance for communication planning management and improvement of communication service quality. However, due to the complex spatiotemporal correlation and uncertainty caused by the spatial topology and dynamic time characteristics of mobile communication networks, traffic prediction is facing enormous challenges. We propose a mobile traffic prediction method using dynamic spatiotemporal synchronous graph convolutional network (DSSGCN). DSSGCN has designed multiple components, which can effectively capture the heterogeneity in the local space-time map. More specifically, the network not only models the dynamic characteristics of nodes in the spatiotemporal graph of network traffic, but also captures the dynamic spatiotemporal characteristics of the edges of mobile service data with different time stamps. The outputs of these two components are fused by collaborative convolution to obtain the prediction results. Experiments on two ground truth mobile traffic datasets show that our DSSGCN model has good prediction performance.
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