Network traffic prediction based on deep belief network in wireless mesh backbone networks
Wireless mesh network is prevalent for providing a decentralized access for users. For a
wireless mesh backbone network, it has obtained extensive attention because of its large …
wireless mesh backbone network, it has obtained extensive attention because of its large …
Network traffic prediction based on deep belief network and spatiotemporal compressive sensing in wireless mesh backbone networks
Wireless mesh network is prevalent for providing a decentralized access for users and other
intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles …
intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles …
Prediction of network traffic in wireless mesh networks using hybrid deep learning model
Wireless mesh networks are getting adopted in the domain of network communication. Their
main benefits include adaptability, configuration, and flexibility, with added efficiency in cost …
main benefits include adaptability, configuration, and flexibility, with added efficiency in cost …
A deep learning method based on an attention mechanism for wireless network traffic prediction
M Li, Y Wang, Z Wang, H Zheng - Ad Hoc Networks, 2020 - Elsevier
With the rapid development of wireless networks, the self-management and active
adjustment capabilities of base stations have become crucial. The accurate prediction of …
adjustment capabilities of base stations have become crucial. The accurate prediction of …
Traffic prediction of wireless cellular networks based on deep transfer learning and cross-domain data
Q Zeng, Q Sun, G Chen, H Duan, C Li, G Song - IEEE access, 2020 - ieeexplore.ieee.org
Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the
5G/B5G field. However, it is very challenging since the wireless cellular traffic usually show …
5G/B5G field. However, it is very challenging since the wireless cellular traffic usually show …
Efficient wireless traffic prediction at the edge: A federated meta-learning approach
Wireless traffic prediction plays a vital role in managing high dynamic and low latency
communication networks, especially in 6G wireless networks. Regarding data and …
communication networks, especially in 6G wireless networks. Regarding data and …
[HTML][HTML] Deep learning based network traffic matrix prediction
D Aloraifan, I Ahmad, E Alrashed - International Journal of Intelligent …, 2021 - Elsevier
Network traffic matrix prediction is a methodology of predicting network traffic behavior
ahead of time in order to improve network management and planning. Different neural …
ahead of time in order to improve network management and planning. Different neural …
Network traffic prediction based on INGARCH model
M Kim - Wireless Networks, 2020 - Springer
In this paper, we introduce the integer-valued generalized autoregressive conditional
heteroscedasticity (INGARCH) as a network traffic prediction model. As the INGARCH is …
heteroscedasticity (INGARCH) as a network traffic prediction model. As the INGARCH is …
Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks
Network traffic analysis has been one of the most crucial techniques for preserving a large-
scale IP backbone network. Despite its importance, large-scale network traffic monitoring …
scale IP backbone network. Despite its importance, large-scale network traffic monitoring …
Spatio-temporal wireless traffic prediction with recurrent neural network
Accurate prediction of user traffic in cellular networks is crucial to improve the system
performance in terms of energy efficiency and resource utilization. However, existing work …
performance in terms of energy efficiency and resource utilization. However, existing work …