Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach

TL;DR

This paper presents a novel data-driven approach using GraphSAGE to reconstruct and label conflict graphs in O-RAN, addressing the challenge of unknown conflicts among third-party RAN applications.

Contribution

It introduces the first method to dynamically learn hidden relationships and conflicts in O-RAN using graph neural networks, improving conflict detection accuracy.

Findings

Effectively reconstructs conflict graphs in O-RAN

Identifies conflicts as defined by O-RAN Alliance

Demonstrates high accuracy in conflict detection

Abstract

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 RIC (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 O-RAN. Specifically, we leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, parameters, and KPIs. Our numerical results, based on a conflict model used in the 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.