AI-Powered Conflict Management in Open RAN: Detection, Classification, and Mitigation

Abdul Wadud; Nima Afraz; Fatemeh Golpayegani

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arXiv · 2602.19758

AI-Powered Conflict Management in Open RAN: Detection, Classification, and Mitigation

Abdul Wadud·Nima Afraz·Fatemeh Golpayegani

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Abstract

Open Radio Access Network (RAN) was designed with native Artificial Intelligence (AI) as a core pillar, enabling AI- driven xApps and rApps to dynamically optimize network performance. However, the independent ICP adjustments made by these applications can inadvertently create conflicts- direct, indirect, and implicit, which lead to network instability and KPI degradation. Traditional rule-based conflict management becomes increasingly impractical as Open RAN scales in terms of xApps, associated ICPs, and relevant KPIs, struggling to handle the complexity of multi-xApp interactions. This highlights the necessity for AI-driven solutions that can efficiently detect, classify, and mitigate conflicts in real-time. This paper proposes an AI-powered framework for conflict detection, classification, and mitigation in Open RAN. We introduce GenC, a synthetic conflict generation framework for large-scale labeled datasets with controlled parameter sharing and realistic class imbalance, enabling robust training and evaluation of AI models. Our classification pipeline leverages GNNs, Bi-LSTM, and SMOTE-enhanced GNNs, with results demonstrating SMOTE-GNN's superior robustness in handling imbalanced data. Experimental validation using both synthetic datasets (5-50 xApps) and realistic ns3-oran simulations with OpenCellID-derived Dublin topology shows that AI-based methods achieve 3.2x faster classification than rule-based approaches while maintaining near-perfect accuracy. Our framework successfully addresses Energy Saving (ES)/Mobility Robustness Optimization (MRO) conflict scenarios using realistic ns3-oran and scales efficiently to large-scale xApp environments. By embedding this workflow into Open RAN's AI-driven architecture, our solution ensures autonomous and self-optimizing conflict management, paving the way for resilient, ultra-low-latency, and energy-efficient 6G networks.

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BibTeX RIS

Abdul Wadud, Nima Afraz, Fatemeh Golpayegani. 2026-02-23. AI-Powered Conflict Management in Open RAN: Detection, Classification, and Mitigation. https://arxiv.org/abs/2602.19758

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