Abstract
The detection of cells with sub-optimal performance and the identification of the root-cause of such performance is a crucial and challenging task in Network Performance Management (NPM). The contemporary NPM approaches, being reactive, silo-based, and highly expert-reliant, are not viable options for such tasks anymore, particularly in the emerging complex heterogeneous mobile networks. The state-of-the-art research in the field of data-driven Artificial Intelligence (AI) is a ray of hope for developing innovative solutions for such NPM tasks. However, the scarcity of holistic and detailed real network data limits the potential of this approach. In this study, we present a comprehensive AI-driven framework for the auto-diagnosis of cells with sub-optimal performance in a real network. We have explored and shared insight about an untapped comprehensive Call Detail Record (CDR) dataset from a real network operator. The outcome is anonymous and annotated data made public to encourage further research in this domain. We employ a K-means clustering method that exploits CDR data and domain experts' input for the identification of particular types of cell performances. Next, a support vector machine-based classifier is developed for real-time applications which classifies the network nodes based on their performance with an accuracy of 97.69%. Subsequently, we introduce an algorithm that uses the classification results for the root-cause analysis of sub-optimal performance by leveraging network topography and area knowledge. The method succeeds in reaching the outcomes of an expert-led root-cause analysis and beyond. At the same time, the algorithmic approach limits the manual root-cause analysis to 30 possible scenarios per hour as opposed to analysis of 759 cells, thus it reduces the workload of an expert significantly. In the broad picture, the proposed AI framework lays the foundation towards zero-touch mobile network and service management starting with automated NPM and root-cause analysis.