WLAN Traffic Prediction Using Support Vector Machine

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概要

• 論文の詳細を見る

• 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.

• 2009-09-01

著者

• Ma Maode

  Nanyang Technological Univ. Singapore

• Ma Maode

  Nanyang Technological University

• Shu Yantai

  Department Of Computer Science Tianjin University

• Feng Huifang

  College Of Mathematics And Information Science Northwest Normal University

関連論文

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• WLAN Traffic Prediction Using Support Vector Machine
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• Study of Medium Access Delay in IEEE802.11 Wireless Networks(Wireless Communication Technologies)
• Wireless Traffic Modeling and Prediction Using Seasonal ARIMA Models(Network)

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