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A credible traffic prediction method based on self-supervised causal discovery

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Next-generation wireless network aims to support low-latency,high-speed data transmission services by incorporating artificial intelligence(AI)technologies.To fulfill this promise,AI-based network traffic prediction is essential for pre-allocating resources,such as bandwidth and computing power.This can help reduce network congestion and improve the quality of service(QoS)for users.Most studies achieve future traffic prediction by exploiting deep learning and reinforcement learning,to mine spatio-temporal correlated variables.Nevertheless,the prediction results obtained only by the spatio-temporal correlated variables cannot reflect real traffic changes.This phenomenon prevents the true prediction variables from being inferred,making the prediction algorithm perform poorly.Inspired by causal science,we propose a novel network traffic prediction method based on self-supervised spatio-temporal causal discovery(SSTCD).We first introduce the Granger causal discovery algorithm to build a causal graph among prediction variables and obtain spatio-temporal causality in the observed data,which reflects the real reasons affecting traffic changes.Next,a graph neural network(GNN)is adopted to incorporate causality for traffic prediction.Furthermore,we propose a self-supervised method to implement causal discovery to to address the challenge of lacking ground-truth causal graphs in the observed data.Experimental results demonstrate the effectiveness of the SSTCD method.

wireless network traffic predictioncausal discoveryself-supervised

Dan WANG、Yingjie LIU、Bin SONG

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State Key Laboratory of Integrated Services Networks,Xidian University,Xi'an 710071,China

Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital EconomyNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaKey Research and Development Program of ShaanxiISN State Key Laboratory

GML-KF-22-0162201419623723572022ZDLGY05-08

2024

10.1007/s11432-023-3899-1

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(5)
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