Traffic Prediction Model Based on Improved Quantum Particle Swarm Algorithm in Wireless Network

Abstract

Abstract With the rapid development of data transmission, how to achieve fast and accurate prediction of wireless network traffic is an important issue to data collection. In this paper, we propose a traffic prediction model, namely improved quantum particle swarm optimization based on BP neural network. We adopt BP neural network as the basic architecture. On this basis, wavelet multi-resolution analysis technology is introduced as the pre-processing method for prediction model input. Aiming at the shortcomings of slow convergence and easy falling into local optimum of BP neural network, the improved quantum particle swarm algorithm is used to optimize it. Simulation experiments show that compared with the traditional algorithms, the proposed model has higher prediction accuracy and faster convergence rate.