Short-term link quality prediction using nonparametric time series analysis

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• 作者：LiNa Weng ; Ping Zhang ; ZhiYong Feng ; HongWei Cheng…
• 关键词：link quality prediction ; intermediate links ; nonparametric modeling ; time series ; functional ; coefficient autoregression ; 閾捐矾璐ㄩ噺棰勬祴 ; 涓棿閾捐矾 ; 闈炲弬鏁板缓妯?/li> 鏃堕棿搴忓垪 ; 鍑芥暟绯绘暟鑷洖褰?/li> 082308
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：58
• 期：8
• 页码：1-15
• 全文大小：1,189 KB
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• 作者单位：LiNa Weng (1) (2)
  Ping Zhang (3)
  ZhiYong Feng (1)
  HongWei Cheng (1)
  Hao Lian (1)
  Bin Fu (1)
  
  1. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China
  2. China Academy of Electronics and Information Technology, Beijing, 100041, China
  3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
  
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

Wireless link quality prediction (LQP) is the foundation for proactive operations and is therefore a key technique in alleviating network performance degradation. However, accurate LQP is difficult because of the dynamic nature of wireless environments. A recent study found that fluctuations in intermediate quality links often show dynamics on a sub-second granularity, making the task even more challenging. In order to leverage the intermediate links, as well as fine-tune upper-layer protocols, we propose to use nonparametric modeling in nonlinear time series analysis that predicts short-term link quality online. Unlike existing studies, we do not define any new experimental or hypothetical models, or train models using a set of training data. Functional-coefficient autoregression is employed to predict the link dynamics at high time resolutions. We apply our approach and a local linear regression-based LQP (a typical parametric modeling approach) to both NS-2 simulation and empirical packet traces. The results indicate that the proposed method has much higher prediction accuracy and convergence speed than the local linear regression-based LQP under dynamic network conditions.