A Machine Learning Driven Methodology for Alarm Prediction Towards Self-Healing in Wireless Networks

Although Artificial Intelligence (AI) is already used by 5
^th
Generation (5G) to support specific network functions, the increased complexity of 6
^th
Generation (6G) will demand the adoption of extended AI capabilities to enhance network efficiency. Moreover, high network performance and availability at a sustainable cost will be crucial to emerging applications, such as autonomous vehicles and smart cities. In this context, operators are expected to implement Self-Healing Operations (SHOs) to transition from reactive handling of network faults to a preventive approach, relying on statistical learning of network data. This paper proposes a Machine Learning (ML)-driven methodology to predict network faults using generic Fault Management (FM) data, enabling the implementation of preventive actions to avoid service degradation or failure. The evaluation of this methodology using live network data revealed statistical associations among certain network faults, considering both time and root-cause factors. Therefore, FM data and two ML models, namely Logistic Regression (LR) and Light Gradient Boosting Model (LGBM), were used to predict network faults, achieving a 93% success rate within a 60-minute anticipation period.