Spline-Extrapolation Method in Traffic Forecasting in 5G Networks

• Autorzy: Strelkovskaya Irina , Solovskaya Irina , Makoganiuk Anastasiya

Identyfikatory

DOI

10.26636/jtit.2019.134719

• Języki publikacji: EN

Abstrakty

EN

This paper considers the problem of predicting self-similar traffic with a significant number of pulsations and the property of long-term dependence, using various spline functions. The research work focused on the process of modeling self-similar traffic handled in a mobile network. A splineextrapolation method based on various spline functions (linear, cubic and cubic B-splines) is proposed to predict selfsimilar traffic outside the period of time in which packet data transmission occurs. Extrapolation of traffic for short- and long-term forecasts is considered. Comparison of the results of the prediction of self-similar traffic using various spline functions has shown that the accuracy of the forecast can be improved through the use of cubic B-splines. The results allow to conclude that it is advisable to use spline extrapolation in predicting self-similar traffic, thereby recommending this method for use in practice in solving traffic prediction-related problems.

Słowa kluczowe

EN

quality of service; self-similar traffic; spline functions; error of recovery

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2019
• Tom: nr 3
• Strony: 8--16
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Strelkovskaya Irina

• O. S. Popov Odessa National Academy of Telecommunications, Kuznechnaya, 1, 65029 Odessa, Ukraine

autor

Solovskaya Irina

• O. S. Popov Odessa National Academy of Telecommunications, Kuznechnaya, 1, 65029 Odessa, Ukraine

autor

Makoganiuk Anastasiya

• O. S. Popov Odessa National Academy of Telecommunications, Kuznechnaya, 1, 65029 Odessa, Ukraine

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).