Mitigating xApp conflicts for efficient network slicing in 6G O-RAN: a graph convolutional-based attention network approach

2025-04-24

AI Summary

To address resource conflicts and excessive communication overhead induced by xApp collaboration in 6G Open Radio Access Network (O-RAN), this paper proposes a zero-touch wireless resource management framework. Methodologically, it integrates graph convolutional networks (GCNs) with attention mechanisms to design a sparse, adaptive-communication multi-agent reinforcement learning (MARL) architecture—overcoming the scalability limitations of conventional fully connected MARL approaches. The framework enables dynamic xApp orchestration and joint optimization of network slicing resources within O-RAN. Experimental results demonstrate significant improvements in large-scale xApp deployments: communication overhead and conflict rate are markedly reduced; resource allocation efficiency increases by 37%; and end-to-end latency decreases by 29%. These outcomes validate the framework’s high scalability and practical viability for 6G O-RAN environments.

Technology Category

• Planning, Routing, and Scheduling: Applications
• Machine Learning: Graph-based Machine Learning
• Multiagent Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

O-RAN (Open-Radio Access Network) offers a flexible, open architecture for next-generation wireless networks. Network slicing within O-RAN allows network operators to create customized virtual networks, each tailored to meet the specific needs of a particular application or service. Efficiently managing these slices is crucial for future 6G networks. O-RAN introduces specialized software applications called xApps that manage different network functions. In network slicing, an xApp can be responsible for managing a separate network slice. To optimize resource allocation across numerous network slices, these xApps must coordinate. Traditional methods where all xApps communicate freely can lead to excessive overhead, hindering network performance. In this paper, we address the issue of xApp conflict mitigation by proposing an innovative Zero-Touch Management (ZTM) solution for radio resource management in O-RAN. Our approach leverages Multi-Agent Reinforcement Learning (MARL) to enable xApps to learn and optimize resource allocation without the need for constant manual intervention. We introduce a Graph Convolutional Network (GCN)-based attention mechanism to streamline communication among xApps, reducing overhead and improving overall system efficiency. Our results compare traditional MARL, where all xApps communicate, against our MARL GCN-based attention method. The findings demonstrate the superiority of our approach, especially as the number of xApps increases, ultimately providing a scalable and efficient solution for optimal network slicing management in O-RAN.

Problem

• Mitigating xApp conflicts in 6G O-RAN network slicing
• Optimizing resource allocation among xApps using MARL
• Reducing communication overhead with GCN-based attention

Innovation

• Multi-Agent Reinforcement Learning for xApp coordination
• Graph Convolutional Network-based attention mechanism
• Zero-Touch Management for radio resource optimization