Fault detection and diagnosis method for heterogeneous wireless network based on GAN

Aiming at the problem that in the process of network fault detection and diagnosis, how to train the precise fault diagnosis and detection model based on small data volume, a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed. Firstly, the common network fault sources in heterogeneous wireless network environment was analyzed, and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm. Then, the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data. Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks, with an accuracy of 98.18%. © 2020, Editorial Board of Journal on Communications. All right reserved.