Data-driven analytics for automated cell outage detection in Self-Organizing Networks

Abstract

In this paper, we address the challenge of autonomous cell outage detection (COD) in Self-Organizing Networks (SON). COD is a pre-requisite to trigger fully automated self-healing recovery actions following cell outages or network failures. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state-of-the-art SON, since it triggers no alarms for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless site visits or drive tests are performed, or complaints are received by affected customers. To address this issue, we present and evaluates a COD framework, which is based on minimization of drive test (MDT) reports, a functionality recently specified in third generation partnership project (3GPP) Release 10, for LTE Networks. Our proposed framework aims to detect cell outages in an autonomous fashion by first pre-processing the MDT measurements using multidimensional scaling method and further employing it together with machine learning algorithms to detect and localize anomalous network behaviour. We validate and demonstrate the effectiveness of our proposed solution using the data obtained from simulating the network under various operational settings.