Conflict Detection in AI-RAN: Efficient Interaction Learning and Autonomous Graph Reconstruction

TL;DR

This paper introduces a novel, efficient framework for conflict detection in AI-native mobile networks, utilizing a two-tower encoder and autonomous graph reconstruction to improve performance and reduce computational complexity.

Contribution

It presents the first systematic approach combining interaction learning and autonomous graph reconstruction for conflict detection in AI-RANs, avoiding complex GNNs and manual thresholds.

Findings

Effective conflict detection without manual thresholds

Reduced computational complexity compared to GNN-based methods

Autonomous conflict graph reconstruction demonstrated

Abstract

Artificial Intelligence (AI)-native mobile networks represent a fundamental step toward 6G, where learning, inference, and decision making are embedded into the Radio Access Network (RAN) itself. In such networks, multiple AI agents optimize the network to achieve distinct and often competing objectives. As such, conflicts become inevitable and have the potential to degrade performance, cause instability, and disrupt service. Current approaches for conflict detection rely on conflict graphs created from relationships between AI agents, parameters, and Key Performance Indicators (KPIs). Existing works often rely on complex and computationally expensive Graph Neural Networks (GNNs) and depend on manually chosen thresholds to create conflict graphs. In this work, we present the first systematic framework for conflict detection in AI-native mobile networks, propose an efficient two-tower encoder architecture for learning interactions based on data from the RAN, and introduce a data-driven sparsity-based mechanism for autonomously reconstructing conflict graphs without manual fine-tuning.