Spatial-temporal Cellular Traffic Prediction: A Novel Method Based on Causality and Graph Attention Network

Chen, Xiangyu; Chuai, Gang; Zhang, Kaisa; Gao, Weidong

doi:10.1109/wcnc55385.2023.10118616

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…Chen et al [10] an innovative approach for predicting cellular traffic by integrating causality analysis and graph attention networks with Gated Recurrent Units (GRU). This method decomposes traffic data into trend, period, and random components.…”

Section: Cellular Traffic Prediction On Gatmentioning

confidence: 99%

Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Zhang,

Zhou,

et al. 2024

Preprint

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This paper addresses the challenges of exponentially growing traffic in cellular networks by proposing a novel predictive model, HGCRN, which combines static graph convolutional recurrent neural network and meta-graph learning. The model is designed to effectively capture the complex spatio-temporal dependencies in network traffic, enhancing prediction accuracy and operational efficiency. By constructing graph adjacency matrices that go beyond mere geographical proximity, HGCRN offers a deeper understanding of the dynamic interactions within the network. Tested on real-world datasets from Telecom Italia and China Mobile, the model demonstrates significant improvements over traditional and state-of-the-art methods in terms of predictive accuracy and reliability.

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Section: Cellular Traffic Prediction On Gatmentioning

confidence: 99%

Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Zhang,

Zhou,

et al. 2024

Preprint

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Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Zhang,

Zhou,

et al. 2024

Wireless Pers Commun

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Content-Aware Network Traffic Prediction Framework for Quality of Service-Aware Dynamic Network Resource Management

Aziz,

Ioannou,

Lestas

et al. 2023

IEEE Access

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Next-generation mobile networks (such as Fifth-Generation (5G), and Sixth-Generation (6G)) are envisioned to undergo an unprecedented transformation from connected things to connected intelligence with more stringent characteristics, i.e., low end-to-end latency, high bandwidth, reliable connectivity, etc. Such networks will significantly increase network traffic in the distribution networks, causing the need for real-time automated decision-making, such as automated network resource allocation. The network resources must be allocated dynamically based on the Quality of Service (QoS) requirements. However, the primary concern is that the distribution networks may get congested soon after allocating network resources. Thus, a QoS-aware prediction framework can be used proactively to predict the future trend of network traffic in each QoS class. In this paper, we propose a framework for predicting the heterogeneous multivariate QoS-aware network traffic to make the best use of network resources dynamically. Specifically, we use a Recurrent Neural Network (RNN) to integrate a Bidirectional Long Short-Term Memory (BLSTM) neural network. The results show that the fusion of RNN-BLSTM can predict the QoS-aware network traffic for over 13 hours with an average accuracy of 97.68%. Moreover, the proposed model is trained and tested over limited data, collected and identified through Deep Packet Inspection (DPI) over an operational network. In addition, we compared the RNN-BLSTM with other prediction algorithms (i.e., LSTM, ARIMA, SVM) in terms of precision, execution time, and energy consumption. Lastly, the proposed framework is used to assign the network resources to each QoS class based on the QoS requirements of that class and its pre-defined priority.INDEX TERMS QoS, heterogeneous multivariate internet traffic, RNN-BLSTM, DPI.

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Spatial-temporal Cellular Traffic Prediction: A Novel Method Based on Causality and Graph Attention Network

Cited by 8 publications

References 19 publications

Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Content-Aware Network Traffic Prediction Framework for Quality of Service-Aware Dynamic Network Resource Management

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