Large-Scale and High-Dimensional Cell Outage Detection in 5G Self-Organizing Networks

Abstract

In this paper, we investigate the cell outage detection in Self-Organizing Networks. The purpose of cell outage detection is to automatically detect whether there exist some failures or degradation in the base stations, such that users could not obtain mobile services, or the obtained mobile services do not fulfill their requirements. The cell outage detection in 5G is with great challenge. The deployment of future 5G mobile communication networks would be heterogeneous and ultra-dense. The mobile communication environments are very complicated. They include the multipath transmission, fading, shadowing, interference, and so on. Users' mobility and usage pattern also vary. In such environments, the mobile data would be large-scale and high-dimensional. Traditional small-scale and low-dimensional anomaly detection methods would be unsuitable. Moreover, operational mobile communication networks should be normal almost all the time. Cell outage would be seldom. Therefore, the normal data and anomaly data would be imbalanced. In this paper, we formulate the cell outage detection problem as an anomaly detection problem. We propose an cell outage detection method using the autoencoder, which is a neural network that is trained by unsupervised learning. The network could be trained in advance even when the cell outage data is still not available. Moreover, the autoencoder is also useful for denoising. This proposed method could thus automatically detect the cell outage in complicated and time-varying mobile wireless communication environments. Comprehensive system-level simulations validate the performance of the pronosed method.