a time series modeling and prediction of wireless network traffic

Clicks: 361
ID: 164034
2009
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Abstract
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.
Reference Key
gowrishankar2009internationala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;S. Gowrishankar;P. S. Satyanarayana
Journal canadian journal of kidney health and disease
Year 2009
DOI
10.3991/ijim.v3i1.284
URL
http://online-journals.org/i-jim/article/view/284
Keywords
flow nonstationary time qos traffic nonlinear

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