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Development of a Hybrid Prediction Mechanism using SMA and EXS Methods for GSM Logical Channel Load Variables
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 109 - Number 1 |
| Year of Publication: 2015 |
| Authors: Garba S, Mu'azu M.b, Dajab D.d |
10.5120/19151-0577
|
Garba S, Mu'azu M.b, Dajab D.d . Development of a Hybrid Prediction Mechanism using SMA and EXS Methods for GSM Logical Channel Load Variables. International Journal of Computer Applications. 109, 1 ( January 2015), 16-24. DOI=10.5120/19151-0577
Abstract
The GSM logical channel load are stochastic (random), distinct in time (Erlang) distribution data; and as such it requires robust means of its prediction. The method employed in this work for the predictions is a hybrid of Simple Moving Average (SMA) and Exponential Smoothing (ExS), which can fit in to predict logical channel load variables with it peculiarities. A three (3) month Data were used in determining the number of observations for the prediction (n) for SMA and smoothing constant (?) for ExS. The determinant values obtained are n = 28, and ? = 0. 077. These values are used to predict the logical control and traffic channels load variables that characterizes its utilization.
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