A development and validation framework for AI/ML-driven rApps in open RAN: a case study on network energy saving

Open radio access network (RAN) leverages the RAN intelligent controller (RIC) to enable artificial intelligence/machine learning (AI/ML)-driven network automation. However, a gap remains between algorithmic research and deployable, standards-compliant rApp prototypes with verifiable behavior. This paper addresses this gap by introducing an integrated development and validation framework that supports the full lifecycle of AI/ML-based rApps, from prototyping to functional verification. The framework includes a standards-compliant non-real-time RIC (Non-RT RIC) architecture with supporting functions, an interface for integrating RAN simulators, and a visualization dashboard that displays system state and control actions, enabling traceability of end-to-end control loops. We demonstrate the framework through a case study involving the design and implementation of a predictive network energy saving rApp. In closed-loop experiments, instrumented logs and visualizations indicate that the control decisions of the rApp adhere to the intended operational logic, allowing repeatable functional validation. We also discuss challenges for real-world deployment and study limitations. Overall, the proposed framework provides a practical methodology and toolset that accelerate the transition from algorithmic concept to deployable, validated rApps, advancing reliable AI/ML solutions within the O-RAN ecosystem and offering direct applicability to energy saving as well as other O-RAN use cases.