An efficient correlation-aware anomaly detection framework in cellular network

Nowadays, the fifth-generation (5G) mobile communication system has obtained prosperous development and deployment, reshaping our daily lives. However, anomalies of cell outages and congestion in 5G critically influence the quality of experience and significantly increase operational expenditures. Although several big data and artificial intelligence-based anomaly detection methods have been proposed for wireless cellular systems, they change distributions of the data and ignore the relevance among user activities, causing anomaly detection ineffective for some cells. In this paper, we propose a highly effective and accurate anomaly detection framework by utilizing generative adversarial networks (GAN) and long short-term memory (LSTM) neural networks. The framework expands the original dataset while simultaneously keeping the distribution of data unchanged, and explores the relevance among user activities to further improve the system performance. The results demonstrate that our framework can achieve 97.16% accuracy and 2.30% false positive rate by utilizing the correlation of user activities and data expansion.