ns3-oran-customizable-db: a tool for building a customizable database for Open RAN simulations

Abstract Simulation is an essential approach for evaluating the performance of applications designed to control and diagnose Radio Access Networks (RANs). Assessing proposed solutions in simulated environments that closely approximate real-world network behavior is crucial for mitigating potential deployment risks. This article aims to extend the capabilities of the “ns3-oran” module in the “ns-3” network simulator. The new functionalities remove restrictions on the type of data that can be exchanged with the Near-RT RIC (Near-Real-Time RAN Intelligent Controller), thereby increasing flexibility for researchers and developers. The ability to collect, store, and analyze heterogeneous data is a key enabler for designing and integrating machine-learning-based solutions into the RAN. With these enhancements, the simulator can support the creation of scenarios tailored to training machine learning models, leveraging performance metrics previously unavailable or not configurable in ns-3. As a result, the proposed “ns3-oran-customizable-db” provides a more powerful simulation environment for advancing AI-driven RAN control and optimization approaches.