Graph-based deep learning for communication networks: A survey
W Jiang - Computer Communications, 2022 - Elsevier
Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …
still many challenges that are not fully solved and new solutions are proposed continuously …
Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Spatial-temporal cellular traffic prediction for 5G and beyond: A graph neural networks-based approach
During the past decade, Industry 4.0 has greatly promoted the improvement of industrial
productivity by introducing advanced communication and network technologies in the …
productivity by introducing advanced communication and network technologies in the …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
MVSTGN: A multi-view spatial-temporal graph network for cellular traffic prediction
Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-
temporal characteristics of cellular traffic. The latest approaches mainly aim to model local …
temporal characteristics of cellular traffic. The latest approaches mainly aim to model local …
[HTML][HTML] Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning
X Zhou, Y Zhang, Z Li, X Wang, J Zhao… - Neural Computing and …, 2022 - Springer
Intelligent cellular traffic prediction is very important for mobile operators to achieve resource
scheduling and allocation. In reality, people often need to predict very large scale of cellular …
scheduling and allocation. In reality, people often need to predict very large scale of cellular …
Multi-scale adaptive graph neural network for multivariate time series forecasting
Multivariate time series (MTS) forecasting plays an important role in the automation and
optimization of intelligent applications. It is a challenging task, as we need to consider both …
optimization of intelligent applications. It is a challenging task, as we need to consider both …
Spatial–temporal dependence and similarity aware traffic flow forecasting
M Liu, G Liu, L Sun - Information Sciences, 2023 - Elsevier
Traffic flow forecasting is the cornerstone of the development of intelligent transportation
systems. Accurate forecasting is conducive to the control and management of urban traffic …
systems. Accurate forecasting is conducive to the control and management of urban traffic …
Traffic prediction-assisted federated deep reinforcement learning for service migration in digital twins-enabled MEC networks
In Mobile Edge Computing (MEC) networks, dynamic service migration can support service
continuity and reduce user-perceived delay. However, service migration in MEC networks …
continuity and reduce user-perceived delay. However, service migration in MEC networks …
Capturing spatial–temporal correlations with Attention based Graph Convolutional Network for network traffic prediction
Y Guo, Y Peng, R Hao, X Tang - Journal of Network and Computer …, 2023 - Elsevier
Network traffic prediction is essential and significant to network management and network
security. Existing prediction methods cannot well capture the temporal–spatial correlations …
security. Existing prediction methods cannot well capture the temporal–spatial correlations …