Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network

Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network

Abstract This paper proposes a wireless network traffic prediction model based on Bayesian Gaussian tensor decomposition and recurrent neural network with rectified linear unit (BGCP-RNN-ReLU model), which can effectively predict the changes in the upstream and downstream network traffic in a short...

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Bibliographic Details
Main Authors: Tao Deng, Mengxuan Wan, Kaiwen Shi, Ling Zhu, Xichen Wang, Xuchu Jiang
Format: Article
Language:English
Published: Springer 2021-08-01
Series:SN Applied Sciences
Subjects:
Wireless traffic
Time series prediction
Tensor decomposition
RNN
Online Access:https://doi.org/10.1007/s42452-021-04761-8

Internet

https://doi.org/10.1007/s42452-021-04761-8

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