Paper
AI-Powered Conflict Management in Open RAN: Detection, Classification, and Mitigation
Authors
Abdul Wadud, Nima Afraz, Fatemeh Golpayegani
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.19758v1</id>\n <title>AI-Powered Conflict Management in Open RAN: Detection, Classification, and Mitigation</title>\n <updated>2026-02-23T12:09:46Z</updated>\n <link href='https://arxiv.org/abs/2602.19758v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.19758v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.NI'/>\n <published>2026-02-23T12:09:46Z</published>\n <arxiv:primary_category term='cs.NI'/>\n <author>\n <name>Abdul Wadud</name>\n </author>\n <author>\n <name>Nima Afraz</name>\n </author>\n <author>\n <name>Fatemeh Golpayegani</name>\n </author>\n </entry>"
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