A fusion model of SWT, QGA and BP neural network for wireless network traffic prediction

In this paper a fusion model by combining the Stationary Wavelet Transform (SWT), Quantum Genetic Algorithm (QGA) and Back-propagation (BP) Neural Network is proposed to forecast wireless network traffic. In order to achieve guaranteed Quality of Service (QoS) in wireless networks, various managing measures can be taken only by knowing the network traffic in advance. This developed fusion model which is called the SWT-QGA-BP model can be efficiently used to assess the future network and provide adequate evidence for wireless network management. By using SWT, the original non-stationary wireless traffic data are transformed into multiple stationary components. With the QGA evolution, the BP neural network is optimized in both architecture and initial parameters. The simulation further indicates the effectiveness of the proposed SWT-QGA-BP fusion model, and the results show that our model can enhance the prediction performance significantly in accuracy.