BER Degradation Prediction Using Random Forest Model in GANA Knowledge Plane Platform for 5G/5G-A Transport Network QoS Assurance

Leveraging Random Forest models, we trained our algorithm on real-world data from optical connections and employed a sliding window approach to forecast degradation steps ahead of time. To address the data imbalance inherent in such networks, we applied data cleaning and augmentation techniques. Our model, evaluated on multiple datasets, demonstrated high accuracy in predicting early-stage BER degradation, facilitating proactive maintenance and improving network reliability. The results underline the efficacy of Random Forest models in anticipating service degradation and minimizing disruption in telecommunications infrastructure. The BER Degradation Prediction Using Random Forest Model, together with other AI/ML algorithms required for Autonomic Management & Control (AMC) of a Multi-Layer Transport Network for 5G and Beyond play roles in the AMC operations of an ETSI GANA (Generic Autonomic Networking Architecture) Knowledge Plane (KP) for a Multi-Layer Transport Network. In this paper, we propose GANA Knowledge Plane autonomic management and control implementation architecture and solution for 5G and beyond transport network, specifically focusing on BER degradation prediction based on our ML algorithm.