Demo: A Drift-Handling Automation in AI/ML Framework Integrated O-RAN

Beyond 5G (B5G) networks increasingly leverage open and disaggregated Radio Access Networks (O-RAN) to enable high-speed, low-latency, and flexible services across diverse use cases. However, the dynamic nature of user traffic and network conditions causes Artificial Intelligence (AI)/ Machine Learning (ML) models in RAN Intelligent Controllers (RICs) to experience performance drift – leading to inefficient radio resource allocation and potential service level agreements (SLA) violations. This work demonstrates a real-time drift detection, analysis, and adaptation by leveraging an AI/ML framework integrated O-RAN. This is achieved with realtime data collection from open interfaces which would be used by intelligent controllers (i.e., Non-RT RIC) for training and inference purposes of AI/ML models. The proposed drift-handling automation monitors network performance metrics to identify model drift with minimal data and computation overheads and then adapts to current network conditions by either retraining the model or replacing it with an available pre-trained model. This approach improves reliability, agility, and automation in multi-vendor B5G deployments by means of drift detection and resolution.