Abstract
To efficiently operate 5G radio access networks (RAN) under a variety of environments and use cases that change over time, it is important to intelligently manage the RAN and reprogram its configuration dynamically as needed. The Non-Real-Time RAN Intelligent Controllers (Non-RT RIC) as defined by O-RAN ALLIANCE can serve a key role towards such programmable RAN, by supporting AI-based rApps that can infer the best RAN operating configurations based on gathered information from RAN. In this demo, we present a series of rApps that we have developed based on O-RAN Software Community (O-RAN SC)'s Non-RT RIC. These rApps together provide localization, UE throughput prediction, and cross-cell interference management functionalities. We have successfully integrated our rApps with a commercial-grade 5G system that is deployed in our campus. Our demo shows significant end-to-end performance gain that can be obtained by using AI-driven rApps in a real-world dynamic environment.