Short term traffic volume prediction in umts networks using the kalman filter algorithm
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Accurate traffic volume prediction in Universal Mobile Telecommunication System (UMTS) networks has ...
Accurate traffic volume prediction in Universal Mobile Telecommunication System (UMTS) networks has
become increasingly important because of its vital role in determining the Quality of Service (QoS)
received by subscribers on these networks. This paper developed a short-term traffic volume prediction
model using the Kalman filter algorithm. The model was implemented in MATLAB and validated using
traffic volume dataset collected from a real telecommunication network using graphical and r2 (coefficient
of determination) approaches. The results indicate that the model performs very well as the predicted
traffic volumes compare very closely with the observed traffic volumes on the graphs. The r2 approach
resulted in r2 values in the range of 0.87 to 0.99 indicating 87% to 99% accuracy which compare very well
with the observed traffic volumes.
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