# O-RAN Xapps Conflict Prediction Using Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2503.03523 · HTML: https://mlsanity.com/paper/2503.03523/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.07385/) (2022)
- [PACIFISTA: Conflict Evaluation and Management in Open RAN](https://mlsanity.com/paper/2405.04395/) (2024)
- [Conflict Mitigation Framework and Conflict Detection in O-RAN Near-RT RIC](https://mlsanity.com/paper/2305.07117/) (2023)
- [COMIX: Generalized Conflict Management in O-RAN xApps -- Architecture, Workflow, and a Power Control case](https://mlsanity.com/paper/2501.14619/) (2025)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach](https://mlsanity.com/paper/2412.14119/) (2024)
- [Adaptive Thresholding Heuristic for KPI Anomaly Detection](https://mlsanity.com/paper/2308.10504/) (2023)
- [xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN](https://mlsanity.com/paper/2407.03068/) (2024)
- [Open RAN xApps Design and Evaluation: Lessons Learnt and Identified Challenges](https://mlsanity.com/paper/2311.04380/) (2023)
- [Conflict Management in the Near-RT-RIC of Open RAN: A Game Theoretic Approach](https://mlsanity.com/paper/2311.13389/) (2023)
- [QACM: QoS-Aware xApp Conflict Mitigation in Open RAN](https://mlsanity.com/paper/2405.07324/) (2024)
- [Detection and mitigation of indirect conflicts between xApps in Open Radio Access Networks](https://mlsanity.com/paper/2305.13464/) (2023)
- [Challenges for Conflict Mitigation in O-RAN's RAN Intelligent Controllers](https://mlsanity.com/paper/2311.17482/) (2023)
- [xApp-Level Conflict Mitigation in O-RAN, a Mobility Driven Energy Saving Case](https://mlsanity.com/paper/2411.03326/) (2024)

## Built on by (most cited first, 5 of 5)

- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition](https://mlsanity.com/paper/2505.01783/) (2025)
- [Detecting Control and Response Events for AI-Enabled Radio Access Networks](https://mlsanity.com/paper/2606.06459/) (2026)
- [Explainable Runtime Dependency Tracking for AI-RAN Conflict Monitoring](https://mlsanity.com/paper/2606.06663/) (2026)

## Abstract (arXiv)

O-RAN hosts many intelligent applications known as eXtended Applications (xApps). xApps are applications that leverage advanced algorithms to make dynamic decisions for network optimization. Each application operates with distinct optimization objectives and is managed by independent operators while accessing shared network resources. Conflicts occur when a deployed xApp's objective interferes with another xApp, resulting in incompatible actions or decisions that may negatively impact network performance. The lack of a unified mechanism to coordinate and prioritize actions across different applications can lead to various types of conflicts. Conflict prediction in O-RAN is the proactive analytical process by which potential interactions or behaviors that may lead to conflicts between network applications are identified in advance, before they manifest in the operational system. In our paper, we introduce a novel data-driven Graph Convolutional Network-based method called GRAPH-based Intelligent xApp Conflict Prediction and Analysis (GRAPHICA). It predicts three types of conflicts and pinpoints the root causes. GRAPHICA captures the complex, hidden dependencies among the xApps, controlled parameters, and key performance indicators in O-RAN to predict potential conflicts. Then, it identifies the root causes contributing to the predicted conflicts. The proposed method is evaluated using highly imbalanced synthetic datasets, in which conflict instances constitute between 40 % and merely 10 % of the data. This evaluation setting is designed to reflect realistic operational environments where conflicts are infrequent, thereby enabling a comprehensive assessment of the model's performance under real-world conditions. Experimental results demonstrate an F1-score over 98 % for the synthesized datasets with varying levels of class imbalance.