On the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networks

Abstract

On the road to the sixth generation of cellular networks (6G), the need to ensure a sustainable usage of natural resources, amid increased competition and cost pressures, has driven the adoption of Self-Healing Mobile Networks to enhance operational efficiency of current and future wireless networks. This paradigm shift relies on Artificial Intelligence (AI) to increase automation of network functions, notably by applying predictive fault detection and automatic root-cause analysis. In this context, this paper proposes a Deep Learning (DL) model for self-healing operations based on a Spatial Graph Convolutional Neural Network (SGCN), which is applied to evaluate the performance degradation of Base Stations (BSs) and uncover the underlying root-causes. The advantages of the proposed DL model are threefold. Firstly, it is especially suited for wireless network applications, leveraging the SGCN to account for spatial dependencies among BSs and their physical characteristics. Secondly, the proposed model offers the flexibility to process diverse types of predictive features, including Performance Management (PM), Fault Management (FM), or other data types. Thirdly, it incorporates an explainability module that pinpoints the input features, such as PM counters, with the most significant influence on BS performance, thereby shedding light on its rootcause factors. The proposed model was evaluated on a live 4G network dataset and the results confirmed its effectiveness in identifying BS performance degradation. An F1-score of 89.6% was achieved in the classification of performance failures, which includes a 27% reduction in false negatives compared to prior research outcomes. In a live network environment, this reduction translates into substantial improvements in Quality of Experience (QoE) for the end users and cost savings for the Mobile Network Operators (MNOs).