DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction
As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2173 |
| _version_ | 1797264813442727936 |
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| author | Zengyu Cai Chunchen Tan Jianwei Zhang Liang Zhu Yuan Feng |
| author_facet | Zengyu Cai Chunchen Tan Jianwei Zhang Liang Zhu Yuan Feng |
| author_sort | Zengyu Cai |
| collection | DOAJ |
| description | As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs. |
| first_indexed | 2024-04-25T00:34:52Z |
| format | Article |
| id | doaj.art-e4bd5488e3da4178998dbe3f465edd4d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:52Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e4bd5488e3da4178998dbe3f465edd4d2024-03-12T16:40:22ZengMDPI AGApplied Sciences2076-34172024-03-01145217310.3390/app14052173DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic PredictionZengyu Cai0Chunchen Tan1Jianwei Zhang2Liang Zhu3Yuan Feng4School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaAs network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.https://www.mdpi.com/2076-3417/14/5/2173cellular network traffic predictiondeep learninggraph neural networkmulti-modal feature fusionattention mechanism |
| spellingShingle | Zengyu Cai Chunchen Tan Jianwei Zhang Liang Zhu Yuan Feng DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction Applied Sciences cellular network traffic prediction deep learning graph neural network multi-modal feature fusion attention mechanism |
| title | DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction |
| title_full | DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction |
| title_fullStr | DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction |
| title_full_unstemmed | DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction |
| title_short | DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction |
| title_sort | dbstgnn att dual branch spatio temporal graph neural network with an attention mechanism for cellular network traffic prediction |
| topic | cellular network traffic prediction deep learning graph neural network multi-modal feature fusion attention mechanism |
| url | https://www.mdpi.com/2076-3417/14/5/2173 |
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