An Experimental O-RAN Environment for Evaluating AI-Driven RAN Control

Open RAN (O-RAN) aims to reduce operational costs and improve interoperability by promoting disaggregated and virtualized Radio Access Network architecture. In this context, development of new xApps is limited by the capabilities of underlying experimental platforms. This paper presents an experimental environment integrating srsRAN and OSC-RIC, two widely used open-source O-RAN software components, and extends srsRAN to expose additional performance metrics via the E2 interface. Such extension enables the periodic reporting of detailed KPIs from the PHY and MAC layers, enhancing RAN observability and offering a cornerstone for evaluating new xApps for closed-loop control and adaptive optimization in 5G networks.