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

TL;DR

This paper introduces a proactive mobility management framework for O-RAN using Graph Neural Networks to predict user-cell links, aiming to improve handover efficiency and resource utilization in 5G networks.

Contribution

It explores and compares GNN models for link prediction in cellular networks, providing insights into their application for proactive handover management in O-RAN.

Findings

GNN models effectively capture dynamic network structures

Comparison of GNNs reveals trade-offs in complexity and accuracy

Proposed approach improves handover decision-making

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 O-RAN networks.