Abstract: In order to solve the problem of difficulty in anomaly detection caused by multidimensional dynamic changes in wireless communication network data, a wireless communication network abnormal data elimination algorithm based on VAE-WGAN ( Variational Autoencoder-Wasserstein Generative Adversarial Network) is proposed. Principal component analysis is used to reduce the dimension of wireless communication network data, and wavelet transform is used to denoise the reduced dimension data. The VAE-WGAN model is built by using VAE module and WGAN module, and the data after dimensionality reduction and denoising are input into the model, and the model will output an anomaly score. When the anomaly score is greater than the anomaly detection threshold, the data will be identified as abnormal data and eliminated, so as to achieve the goal of eliminating abnormal data. The experimental results show that the data processing effect of the proposed algorithm is good, and the abnormal data can be effectively detected and accurately eliminated.

Key words: principal component analysis method, wavelet transform, wireless communication network, semi supervised learning, abnormal data removal