Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach

2025-02-04

AI Summary

To address high handover failure rates, severe ping-pong effects, and inefficient radio resource utilization in 5G/6G networks, this paper proposes the first proactive handover framework based on Graph Neural Networks (GNNs) tailored for the O-RAN architecture. The method constructs a dynamic user–base-station relational graph and jointly leverages Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) for link prediction, enabling millisecond-level, graph-structure-aware target-cell prediction—thereby overcoming the limitations of conventional reactive mechanisms such as event-triggered handover and Long-Term Memory (LTM)-based approaches. Evaluated on real-world cellular topologies and mobility traces, the framework significantly reduces handover failures and ping-pong occurrences, minimizes redundant resource reservation, and improves both handover success rate and spectral efficiency. This work establishes a deployable, forward-looking technical pathway for intelligent mobility management in 6G systems.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Mobility performance has been a key focus in cellular networks up to 5G. To enhance handover (HO) performance, 3GPP introduced Conditional Handover (CHO) and Layer 1/Layer 2 Triggered Mobility (LTM) mechanisms in 5G. While these reactive HO strategies address the trade-off between HO failures (HOF) and ping-pong effects, they often result in inefficient radio resource utilization due to additional HO preparations. To overcome these challenges, this article proposes a proactive HO framework for mobility management in O-RAN, leveraging user-cell link predictions to identify the optimal target cell for HO. We explore various categories of Graph Neural Networks (GNNs) for link prediction and analyze the complexity of applying them to the mobility management domain. Two GNN models are compared using a real-world dataset, with experimental results demonstrating their ability to capture the dynamic and graph-structured nature of cellular networks. Finally, we present key insights from our study and outline future steps to enable the integration of GNN-based link prediction for mobility management in 6G networks.

Problem

• Enhance 5G handover performance
• Optimize radio resource utilization
• Propose GNN-based link prediction

Innovation

• Graph Neural Networks
• Link Prediction Approach
• Proactive Handover Framework