O-RAN Xapps Conflict Prediction Using Graph Convolutional Networks

Emmanuel Thepie Fapi; Jun Yan; Maryam Al Shami

RECORD

O-RAN Xapps Conflict Prediction Using Graph Convolutional Networks

Maryam Al Shami, Jun Yan, Emmanuel Thepie Fapi

Networking and Internet Architecture · Submitted 5 Mar 2025

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results demonstrate an F1-score over 98 % for the synthesized datasets with varying levels of class imbalance.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗

Cite ▾

arXiv ID2503.03523 (version 3)

Pith IDQEIVURB2, Pith's permanent record of this paper and its review

CitationEmmanuel Thepie Fapi, Jun Yan, Maryam Al Shami. "O-RAN Xapps Conflict Prediction Using Graph Convolutional Networks." arXiv:2503.03523 (2025). https://pith.science/paper/QEIVURB2

Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 8,793 input tokens.

What the abstract itself states, as probabilities: claims first 0.16 · states limitation 0.44 · human participants 0.03 · controlled comparison 0.05 · randomized assignment 0.02 · states quantitative result 0.99 · compares with prior results 0.06 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	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.	background 1.00	0.04	0.02	asserted 1.00
s1	Each application operates with distinct optimization objectives and is managed by independent operators while accessing shared network resources.	background 1.00	0.05	0.01	asserted 1.00
s2	Conflicts occur when a deployed xApp's objective interferes with another xApp, resulting in incompatible actions or decisions that may negatively impact network performance.	background 1.00	0.04	0.02	asserted 0.87
s3	The lack of a unified mechanism to coordinate and prioritize actions across different applications can lead to various types of conflicts.	gap 0.55	0.06	0.02	hedged possibility 0.86
s4	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.	background 1.00	0.06	0.03	asserted 0.99
s5	In our paper, we introduce a novel data-driven Graph Convolutional Network-based method called GRAPH-based Intelligent xApp Conflict Prediction and Analysis (GRAPHICA).	contribution 0.94	0.94	0.08	asserted 1.00
s6	It predicts three types of conflicts and pinpoints the root causes.	contribution 0.84	0.89	0.17	asserted 1.00
s7	GRAPHICA captures the complex, hidden dependencies among the xApps, controlled parameters, and key performance indicators in O-RAN to predict potential conflicts.	method 0.99	0.75	0.10	asserted 1.00
s8	Then, it identifies the root causes contributing to the predicted conflicts.	method 0.88	0.75	0.06	asserted 1.00
s9	The proposed method is evaluated using highly imbalanced synthetic datasets, in which conflict instances constitute between 40 % and merely 10 % of the data.	method 1.00	0.93	0.04	asserted 1.00
s10	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.	method 0.81	0.62	0.03	asserted 0.99
s11	Experimental results demonstrate an F1-score over 98 % for the synthesized datasets with varying levels of class imbalance.	result 1.00	0.96	0.58	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.53.

What was examined, and what was not

sources read

arXiv listing abstract v3

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

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.

Human review

How Pith checks papers → · Full audit of this paper →

References

How the literature uses it

1 arXiv paper cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Uses its method or tool 1 paper

We leverage the dataset construction framework proposed in [ref] , which simulates O-RAN behavior by modeling the interactions among xApps, network control parameters, and KPIs.

Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition · Task Description · 0.84

Uses its data or values 1 paper

In this section, we aim to evaluate the effectiveness of the proposed C-PP-COAD framework by conducting experiments for two distinct applications: thyroid disease detection [ref] , and conflict detection in O-RAN systems [ref] .

Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition · Experiments · 1.00

What it cites · 7 sentences without a confident label

Compares with 1 paper

Compared to other works such as the one presented in [ref] , which claims an exceptionally high performance of 100%, our method is not inherently underperforming.

Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach · Discussions · 0.92

Cites as background 2 papers

The authors in [ref] propose a method to detect, characterize, and mitigate conflicts among applications in O-RAN.

2405.04395 · Introduction · 0.80
In [ref] , a Deep Q-Network and a multi-headed multilayer perceptron network model are presented.

xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN · Introduction · 0.90

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.