5G RAN Failure Prediction System

The 5G Radio Access Network (RAN) Failure Prediction System leverages advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms to forecast network failures up to two hours in advance, ensuring uninterrupted service and high Quality of Service (QoS). By utilizing Fault Management (FM) and Performance Management (PM) data sourced from Third Generation Partnership Project (3GPP) Service Level Agreements (SLAs), the system predicts potential RAN issues before they impact network performance. The process begins with the collection and preprocessing of FM and PM data from diverse 5G RAN components. This data is cleansed, normalized, and integrated, addressing any missing values or outliers to ensure accuracy. Feature engineering is employed to extract and refine features, highlighting complex relationships within the data. The model development phase combines interpretable models such as Decision Trees, Rule Fit, and Local Interpretable Model-agnostic Explanations (LIME) with anomaly detection algorithms like Isolation Forest. Additionally, dynamic thresholding and temporal analysis using Long Short-Term Memory (LSTM) networks capture time-dependent patterns. Automated feature engineering tools, including the Tree-based Pipeline Optimization Tool (TPOT), enhance the feature extraction process, while AI methods like SHapley Additive exPlanations (SHAP) and Layer-wise Relevance Propagation (LRP) ensure model transparency. Continuous evaluation mechanisms, such as A/B testing, drift detection, and Federated Learning (FL) techniques, are employed to refine the model continually. Once developed, the predictive system is seamlessly integrated into the 5G RAN infrastructure, with regular updates and maintenance to uphold its reliability. This system significantly advances 5G network management by minimizing downtime, optimizing maintenance efforts, and enhancing QoS. Despite challenges like data dependence and complex initial setup, the benefits of proactive maintenance, cost savings, scalability, and transparent insights make this solution a vital tool for the future of 5G networks.