Graph Attention Spatial-Temporal Network for Deep Learning Based Mobile Traffic Prediction

He, Kaiwen; Huang, Yu‐Fen; Chen, Xu; Zhou, Zhi; Yu, Shuai

doi:10.1109/globecom38437.2019.9013136

Cited by 25 publications

(10 citation statements)

References 14 publications

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“…Traffic prediction is also considered in cellular networks, with GNN-based solutions being proposed in recent years [50,31,15,30]. As a prediction problem, the temporal dependencies may be modeled by a recurrent neural network, e.g., Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU).…”

Section: Cellular Networkmentioning

confidence: 99%

“…As an improvement over baselines, GNN is capable of modeling the spatial correlation between different nodes, e.g., a cell tower or an access point. Different structures have been explored in existing studies, e.g., GAT in [50,31], GCN in [15], and GraphSAGE in [30].…”

Section: Cellular Networkmentioning

confidence: 99%

“…Without deploying additional infrastructure, D2D network is promising for provide communication services with an ultra-low latency. How- Network Slicing [61] DQN with GAT GAT [89] Power Control [60] Heterogeneous GNNs HetGAT [96] Routing [17] GCLR MPNN [90] Traffic Prediction [30] Graph-based TCN GraphSAGE [84] Traffic Prediction [50,31] GASTN S-RNN [95] Traffic Prediction [15] DC-STGCN GCN [88] User Association, Power Control [62] Unsupervised Graph Model GraphSAGE [84] VNE [73,46] DRL with GCN GCN [88] ever, there are still many challenges for this objective to happen. To minimize the content fetching delay in D2D network, the joint optimization of cooperative caching and fetching is considered in [35] and a DRL-based algorithm is proposed.…”

Section: Other Wireless Networkmentioning

confidence: 99%

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Graph-based Deep Learning for Communication Networks: A Survey

Jiang

2021

Preprint

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Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active research area. In recent years, to model the network topology, graph-based deep learning has achieved state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different communication networks, e.g. wireless networks, wired networks, and software-defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.

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Section: Cellular Networkmentioning

confidence: 99%

Section: Cellular Networkmentioning

confidence: 99%

Section: Other Wireless Networkmentioning

confidence: 99%

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Graph-based Deep Learning for Communication Networks: A Survey

Jiang

2021

Preprint

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“…However, linear prediction methods have difficulty in capturing non-linear features, such as rapid fluctuations, and the time-dependence of network traffic. Non-linear prediction techniques have emerged with the introduction of artificial neural networks [ 6 , 7 , 8 ], for example, data-driven deep learning models, such as convolutional neural networks (CNNs) [ 9 ] and recurrent neural networks (RNNs) [ 10 , 11 ], in addition to machine learning algorithms, such as support vector regression (SVR) [ 12 ] and Transformer [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

Pan

Wang

Shi

et al. 2022

Sensors

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Network traffic prediction is an important tool for the management and control of IoT, and timely and accurate traffic prediction models play a crucial role in improving the IoT service quality. The degree of burstiness in intelligent network traffic is high, which creates problems for prediction. To address the problem faced by traditional statistical models, which cannot effectively extract traffic features when dealing with inadequate sample data, in addition to the poor interpretability of deep models, this paper proposes a prediction model (fusion prior knowledge network) that incorporates prior knowledge into the neural network training process. The model takes the self-similarity of network traffic as a priori knowledge, incorporates it into the gating mechanism of the long short-term memory neural network, and combines a one-dimensional convolutional neural network with an attention mechanism to extract the temporal features of the traffic sequence. The experiments show that the model can better recover the characteristics of the original data. Compared with the traditional prediction model, the proposed model can better describe the trend of network traffic. In addition, the model produces an interpretable prediction result with an absolute correction factor of 76.4%, which is at least 10% better than the traditional statistical model.

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“…is model integrates spatial-temporal characteristics, characterizes spatial correlation through geographical relationship graphs, characterizes temporal correlation through recurrent neural networks, and predicts network traffic by combining spatiotemporal characteristics [14]. Yang et al proposed a network traffic prediction model combining a graph convolution neural network (GCN) and a gate control recursive unit (GRU).…”

Section: Introductionmentioning

confidence: 99%

AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

Shi

Pan

et al. 2021

Security and Communication Networks

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Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). The AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. The experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient ( R 2 ), and explained variance score (EVS), and has the ability of long-term prediction.

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Graph Attention Spatial-Temporal Network for Deep Learning Based Mobile Traffic Prediction

Cited by 25 publications

References 14 publications

Graph-based Deep Learning for Communication Networks: A Survey

Graph-based Deep Learning for Communication Networks: A Survey

Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

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