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

2024-12-18

arXiv.org

AI Summary

In O-RAN’s near-real-time RIC, multiple xApps often concurrently regulate the same radio parameters, leading to implicit control conflicts—challenging to detect because existing approaches rely on handcrafted rules, fail to capture complex nonlinear relationships between parameters and KPIs, and cannot identify previously unknown conflicts. Method: We propose the first prior-free, data-driven framework for automatic construction and labeling of conflict graphs. Leveraging GraphSAGE, we design a heterogeneous temporal graph neural network that jointly encodes xApp behavioral logs, parameter adjustment trajectories, and KPI response sequences to dynamically learn latent dependencies among xApps, parameters, and KPIs. Contribution/Results: Evaluated on a standard O-RAN conflict benchmark, our method achieves a 92.3% F1-score—substantially outperforming rule-based and conventional GNN baselines. It enables interpretable identification of hidden conflicts and supports online evolutionary conflict analysis—marking the first solution with such capabilities.

Technology Category

Machine Learning: Graph-based Machine LearningPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Algorithm Configuration

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• O-RAN architecture
• mutual interference
• control settings and network performance correlation

Innovation

• Data-Driven Approach
• GraphSAGE Framework
• Conflict Graph Reconstruction