Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks
Wireless mesh network is prevalent for providing a decentralized access for users. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep belief network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low pass component of network traffic that describes the long range dependence of itself. Then a prediction model is built by learning a deep belief network from the extracted low pass component. Otherwise, for the rest high pass component that expresses the gusty and irregular fluctuations of network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high pass component by the built model. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
2017 IEEE Wireless Communications and Networking Conference (WCNC)