Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

Zhang, Chuanting; Zhang, Haixia; Yuan, Di; Zhang, Minggao

doi:10.1109/lcomm.2018.2841832

Cited by 179 publications

(97 citation statements)

References 11 publications

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“…Figure 4 shows the Pearson correlation coefficient ρ for the spatial correlation between target cell x (i,j) and its neighboring cells x (i ,j ) . The Pearson correlation coefficient is a widely used metric [16,17]. Its definition is expressed as follows:…”

Section: Temporal Domainmentioning

confidence: 99%

“…The experimental dataset was from Telecom Italia, and we used the same pre-processing method as [17] to aggregate data from the 10 min interval in the original dataset to hours. Because the 10 min interval dataset was quite sparse, it was not conducive to extracting spatiotemporal characteristics.…”

Section: Experimental Process and Parameter Settingmentioning

confidence: 99%

“…However, the processing of self-encoding will lose some of the original information, and the ability to extract spatial features needs to be improved. In 2018, Zhang et al [17] proposed a cellular traffic prediction method based on a convolutional neural network (STDenseNet); however, this method did not consider the impact of external conditions on traffic prediction, and the traditional convolution method had a limited effect on the complex spatial characteristics of cellular traffic.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

et al. 2020

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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.

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Section: Temporal Domainmentioning

confidence: 99%

Section: Experimental Process and Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

et al. 2020

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“…It is observed that the CDRs activities are not distributed uniformly over the city of Milan because of different dynamics of Milan sub-regions such as commercial, residential, rural etc. A popular and widely adapted parameter, Pearson correlation coefficient r is used for measuring the variation in spatial correlation [28]. The variation in spatial correlation among the targeted grids, for example, the grids in the center of the city, and grids in the surrounding of the targeted grids is calculated with Pearson correlation coefficient.…”

Section: Spatial Correlationmentioning

confidence: 99%

Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization

et al. 2019

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The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation.

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“…Ali Yadavar Nikravesh et al compared multi‐layer perceptron (MLP), multi‐layer perceptron with weight decay (MLPWD), and support vector machines (SVM) and found the advantage of each prediction method. Besides, Chuanting Zhang used convolutional neural networks (CNN) to predict cellular traffic usage. In addition, location modeling and prediction methods such as using historical choices to guide future choices can also be used in predicting traffic usage …”

Section: Related Workmentioning

confidence: 99%

Behavior predictability and consistency of mobile users' traffic usage between different years based on entropy theory

Cui

Nian

et al. 2019

Int J Communication

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Predicting mobile users' future traffic usage plays an important role for mobile operators to optimize their services. Although a variety of prediction methods have been proposed and applied to this issue, none of them could answer whether the users' traffic behaviors can be predicted. By employing the entropy theory, this paper analyzes the predictability of users' behaviors on traffic types and accounts. Moreover, the mobile network traffic data collected from a major city in China is utilized to test the entropy theory and reveal the accuracy levels. Furthermore, by comparing the prediction results between the years 2012 and 2014, a conclusion is drawn that a large portion of users' behaviors can be predicted, but their traffic usage habits become more difficult to predict in 2014 than in 2012. The results also imply that different entropies achieve more similar predictability in 2014 than in 2012, which indicates the consistency of users' behaviors. Overall, this study reveals the predictability and consistency of users' behavior, which is of great significance for mobile operators.

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Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

Cited by 179 publications

References 11 publications

Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization

Behavior predictability and consistency of mobile users' traffic usage between different years based on entropy theory

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