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

Abdul Wadud, Nima Afraz, Fatemeh Golpayegani

TL;DR

GenC is introduced, 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 and embedding this workflow into Open RAN's AI-driven architecture ensures autonomous and self-optimizing conflict management.

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.

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

TL;DR

GenC is introduced, 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 and embedding this workflow into Open RAN's AI-driven architecture ensures autonomous and self-optimizing conflict management.

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.
Paper Structure (44 sections, 4 equations, 10 figures, 7 tables, 1 algorithm)

This paper contains 44 sections, 4 equations, 10 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Understanding Conflicts among xApps
  3. Conflict Management in Open RAN
  4. System Model and Problem Formulation
  5. System Model
  6. Problem Formulation
  7. AI-Powered Conflict-Management Control Loop
  8. Step 1 – Data Collection and Contextualization
  9. Step 2 – On/Offline Model Training (Non-RT RIC)
  10. Step 3 – Model On-Boarding and Governance
  11. Step 4 – Near-RT Inference and Enforcement
  12. Step 5 – Closed-Loop Feedback and Continuous Learning
  13. Traditional Rule-Based Versus AI-Based Conflict Management in Open RAN
  14. Conflict Detection: From Reactive Thresholding to Proactive Prediction
  15. Conventional Approach: KPI Thresholding
  16. ...and 29 more sections

Figures (10)

  • Figure 1: Conflict Management System in the Near-RT-RIC.
  • Figure 2: Various xApp conflicts in Open RAN.
  • Figure 3: AI-Powered Conflict Management Control-Loop in Open RAN
  • Figure 4: Traditional Rule-Based Versus AI-Based Conflict Management Cycle in O-RAN
  • Figure 5: Architecture of GNN Method with and without SMOTE.
  • ...and 5 more figures