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Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

School of Software, Yunnan University, Kunming 650504, China
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Algorithms 2020, 13(1), 20; https://doi.org/10.3390/a13010020
Received: 27 November 2019 / Revised: 22 December 2019 / Accepted: 6 January 2020 / Published: 8 January 2020
(This article belongs to the Special Issue Networks, Communication, and Computing vol. 2)
With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE.
Keywords: communication traffic prediction; intelligent traffic management; deformable convolution; attention mechanism communication traffic prediction; intelligent traffic management; deformable convolution; attention mechanism
MDPI and ACS Style

Zhang, D.; Liu, L.; Xie, C.; Yang, B.; Liu, Q. Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network. Algorithms 2020, 13, 20.

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