WLAN Traffic Prediction Using Support Vector Machine

Huifang FENG
Yantai SHU
Maode MA

IEICE TRANSACTIONS on Communications   Vol.E92-B    No.9    pp.2915-2921
Publication Date: 2009/09/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E92.B.2915
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Terrestrial Radio Communications
WLAN traffic,  one-step-ahead prediction,  multi-step-ahead prediction,  Support Vector Machine,  

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The predictability of network traffic is an important and widely studied topic because it can lead to the solutions to get more efficient dynamic bandwidth allocation, admission control, congestion control and better performance wireless networks. Support vector machine (SVM) is a novel type of learning machine based on statistical learning theory, can solve small-sample learning problems. The work presented in this paper aims to examine the feasibility of applying SVM to predict actual WLAN traffic. We study one-step-ahead prediction and multi-step-ahead prediction without any assumption on the statistical property of actual WLAN traffic. We also evaluate the performance of different prediction models such as ARIMA, FARIMA, artificial neural network, and wavelet-based model using three actual WLAN traffic. The results show that the SVM-based model for predicting WLAN traffic is reasonable and feasible and has the best performance among the above mentioned prediction models.