A Spatial-Temporal Transformer Network for City-Level Cellular Traffic Analysis and Prediction

Authors

Bo Gu, Sun Yat-Sen University
Junhui Zhan, Sun Yat-Sen University
Shimin Gong, Sun Yat-Sen University
Wanquan Liu, Sun Yat-Sen University
Zhou Su, Xi'an Jiaotong University
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Wireless Communications

Abstract

With the accelerated popularization of 5G applications, accurate cellular traffic prediction is becoming increasingly important for efficient network management. Currently, the latest algorithms for cellular traffic prediction generally neglect extraction of the shallow features of cellular traffic and the prediction accuracy is hence limited. Therefore, we propose a global-local spatial-temporal transformer network (GLSTTN) that can fully excavate diverse spatial-temporal characteristics of cellular traffic for accurate cellular traffic prediction. Specifically, GLSTTN achieves this goal by constructing two modules: the global spatial-temporal module and the local spatial-temporal module. In the global spatial-temporal module, GLSTTN captures global correlations using stacked spatial-temporal blocks, where each block is composed of one spatial transformer and one temporal transformer. A skip connection is then used in each block to strengthen feature propagation. In the local spatial-temporal module, GLSTTN fully extracts the local spatial-temporal dependencies hidden in globally encoded features using densely connected convolutional neural networks. Extensive experiments demonstrate that GLSTTN achieves more accurate cellular traffic prediction than existing approaches on a real-world cellular traffic dataset.

First Page

1

Last Page

1

DOI

10.1109/TWC.2023.3270441

Publication Date

5-2-2023

Keywords

Allocation of Network Resources, Cellular Traffic Prediction, Correlation, Excavation, Feature extraction, Spatial-Temporal Network, Task analysis, Transformer, Transformers, Urban areas, Wireless communication

Comments

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Recommended Citation

B. Gu, J. Zhan, S. Gong, W. Liu, Z. Su and M. Guizani, "A Spatial-Temporal Transformer Network for City-Level Cellular Traffic Analysis and Prediction," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2023.3270441.

Additional Links

https://doi.org/10.1109/TWC.2023.3270441