AI/ML Data-driven Control Loop for Managing O-RAN SDR-based RANs.

Open Radio Access Network (O-RAN) introduced a common control and management overlay, allowing mobile network operators to embed networking intelligence using different types of third-party applications: xApps for real-time control loops, and rApps for Artificial Intelligence (AI)/Machine Learning (ML)-based classification and decision-making. However, the development of reference implementations for rApps lags behind the progress in other O-RAN-related standardization efforts. In this demonstration, we showcase a proof-of-concept rApp capable of generating policies to steer the behavior of xApps, and detail how we extended a RAN slicing xApp to react to such policies, creating the first experimental ML-based RAN slicing platform based on O-RAN.