Mobile networks are transitioning towards open, disaggregated architectures with distributed components, and a key example of this evolution is Open Radio Access Network (O-RAN) architecture. Open Radio Access Network (O-RAN) enables the deployment of third-party applications to help improve performance and flexibility. However, the operation of these third-party applications, known as xApps, may result in conflicting interactions, especially when they try to control the same network settings to achieve different goals. Such interactions can lead to performance degradation and network instability. The current solutions for detecting and mitigating conflicts in the literature assume that all conflicts are known beforehand, which is not always the case due to complex and often hidden relationships between control parameters and performance metrics. In this paper, we introduce a data-driven method for reconstructing and labeling conflict graphs in Open Radio Access Network (O-RAN). We leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, parameters, and Key Performance Indicators (KPIs). Our numerical results demonstrate that our proposed method can effectively reconstruct conflict graphs and identify the conflicts defined by the Open Radio Access Network (O-RAN) Alliance.

The Open Radio Access Network (O-RAN) architecture enables the deployment of third-party applications on the RAN Intelligent Controllers (RICs). However, the operation of third-party applications in the Near Real-Time RAN Intelligent Controller (Near-RT RIC), known as xApps, may result in conflicting interactions. Each xApp can independently modify the same control parameters to achieve distinct outcomes, which has the potential to cause performance degradation and network instability. The current conflict detection and mitigation solutions in the literature assume that all conflicts are known a priori, which does not always hold due to complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we introduce the first data-driven method for reconstructing and labeling conflict graphs in Open Radio Access Network (O-RAN). Specifically, we leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, control parameters, and Key Performance Indicators (KPIs). Our numerical results, based on a conflict model used in the Open Radio Access Network (O-RAN) conflict management literature, demonstrate that our proposed method can effectively reconstruct conflict graphs and identify the conflicts defined by the O-RAN Alliance.

Master of Science