Mobile Network Traffic Prediction Based on Seasonal Adjacent Windows Sampling and Conditional Probability Estimation

2021 ◽  
pp. 1-1
Author(s):  
Jin Huang ◽  
Ming Xiao
Keyword(s):  
Conditional Probability ◽  
Network Traffic ◽  
Mobile Network ◽  
Probability Estimation ◽  
Traffic Prediction

scholarly journals An Experimental Investigation of Mobile Network Traffic Prediction Accuracy

2016 ◽  
Vol 3 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Ali Yadavar Nikravesh ◽  
◽  
Samuel A. Ajila ◽  
Chung-Horng Lung ◽  
Wayne Ding ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
Network Traffic ◽  
Prediction Accuracy ◽  
Mobile Network ◽  
Traffic Prediction

Network traffic prediction based on wavelet FARIMA model

2011 ◽  
Vol 31 (4) ◽  
pp. 901-903
Author(s):  
Yong SUN ◽  
Guang-wei BAI ◽  
Lu ZHAO
Keyword(s):  
Network Traffic ◽  
Traffic Prediction

scholarly journals Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingtian Zeng ◽  
Qiang Sun ◽  
Geng Chen ◽  
Hua Duan
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Traffic ◽  
Cellular Network ◽  
Critical Issue ◽  
Traffic Prediction ◽  
Weekly Cycle ◽  
Cross Domain ◽  
Cellular Traffic

AbstractWireless 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 shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

scholarly journals Autoregressive models of network traffic prediction

2021 ◽  
Vol 1864 (1) ◽  
pp. 012099
Author(s):  
T. Tatarnikova ◽  
B. Sovetov ◽  
V. Chehanovsky
Keyword(s):  
Network Traffic ◽  
Autoregressive Models ◽  
Traffic Prediction

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

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