amsys 17(14): e3

Research Article

A novel Self-Similar Traffic Prediction Method Based on Wavelet Transform for Satellite Internet

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  • @ARTICLE{10.4108/eai.28-8-2017.153306,
        author={Cong Li and Yu Han and Zhenming Sun and Zhenyong Wang},
        title={A novel Self-Similar Traffic Prediction Method Based on Wavelet Transform for Satellite Internet},
        journal={EAI Endorsed Transactions on Ambient Systems},
        volume={4},
        number={14},
        publisher={EAI},
        journal_a={AMSYS},
        year={2017},
        month={8},
        keywords={satellite internet, self-similar traffic prediction, wavelet transform, ARIMA model},
        doi={10.4108/eai.28-8-2017.153306}
    }
    
  • Cong Li
    Yu Han
    Zhenming Sun
    Zhenyong Wang
    Year: 2017
    A novel Self-Similar Traffic Prediction Method Based on Wavelet Transform for Satellite Internet
    AMSYS
    EAI
    DOI: 10.4108/eai.28-8-2017.153306
Cong Li1, Yu Han2,*, Zhenming Sun1, Zhenyong Wang2,3
  • 1: Beijing Electro-Mechanical Engineering Institute, Beijing, China
  • 2: Harbin Institute of Technology, Harbin, China
  • 3: Shenzhen Academy of Aerospace Technology, Shenzhen, China
*Contact email: 13B905008@hit.edu.cn

Abstract

With service types and requirements of broadband satellite internet continuously increasing, improving QoS (Quality of Service) of satellite internet has attracted extensive attention. To reduce the impact of self-similarity caused by various of service traffic sources converging on satellite communication system, this paper establishes a novel model from the perspective of self-similar traffic prediction. A method combinating wavelet transform and ARIMA (Autoregressive Integrated Moving Average) model to predict self-similar traffic of satellite internet is proposed. The optimal prediction model is presented. The number selection of prediction samples and the impact of prediction steps on the accuracy of the prediction system are discussed, and the parameters are addressed. Simulation results show ARIMA model with a combination of wavelet transform can achieve a better prediction than that of the traditional autoregressive model, not utilizing wavelet technology.