A Time Series Modeling and Prediction of Wireless Network Traffic

A Time Series Modeling and Prediction of Wireless Network Traffic

Traffic flow

Nonlinear and Nonstationary Time Series

QoS

FARIMA

RRBFN

ESN and Prediction

The number of users and their network utilization will enumerate the traffic of the network. The accurate and timely estimation of network traffic is increasingly becoming important in achieving guaranteed Quality of Service (QoS) in a wireless network. The better QoS can be maintained in the network by admission control, inter or intra network handovers by knowing the network traffic in advance. Here wireless network traffic is modeled as a nonlinear and nonstationary time series. In this framework, network traffic is predicted using neural network and statistical methods. The results of both the methods are compared on different time scales or time granularity. The Neural Network(NN) architectures used in this study are Recurrent Radial Basis Function Network (RRBFN) and Echo state network (ESN).The statistical model used here in this work is Fractional Auto Regressive Integrated Moving Average (FARIMA) model. The traffic prediction accuracy of neural network and statistical models are in the range of 96.4% to 98.3% and 78.5% to 80.2% respectively.

Gowrishankar, S.

Satyanarayana, P. S.

International Journal of Interactive Mobile Technologies (iJIM); Vol. 3 No. 1 (2009); pp. 53-62

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2008-11-14

Copyright (c) 2017 S. Gowrishankar, P. S. Satyanarayana

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Peer-reviewed Article