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Spatial-Temporal Aggregation Graph Convolution Network for Efficient Mobile Cellular Traffic Prediction
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DOI：10.1109/LCOMM.2021.3138075.png)
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Abstract
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Accurate cellular traffic prediction is challenging due to the complex spatial topology of cellular network and the dynamic temporal feature of mobile traffic. To overcome these problems, this letter proposes a spatial-temporal aggregation graph convolution network (STAGCN), in which the daily historical pattern and the hourly current-day pattern of mobile traffic are modeled. Moreover, the complex spatial-temporal correlation is captured by an aggregation graph convolution network for all nodes across different timestamps. The external factors' impact on mobile traffic is fed into a regression module at the last step to obtain the predicted traffic. Experimental results show that the proposed model can achieve better prediction performance than conventional methods with superior training efficiency.
Keywords:
Correlation
Convolution
Predictive models
Feature extraction
Urban areas
Time series analysis
Research and development
Mobile traffic prediction
deep learning
spatial-temporal correlation
graph convolution network
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
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