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

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 selfsimilarity 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. Received on 14 August 2017; accepted on 28 September 2017; published on 28 August 2017