Graph Neural Networks for Open Radio Access Network Mobility Management: A Link Prediction Approach

Mobility performance has been a key focus in cellular networks up to 5G. To enhance handover (HO) performance, the Third Generation Partnership Project introduced a conditional HO and layer 1/layer 2 (L1/L2)-triggered mobility (LTM) mechanisms in 5G. While these reactive HO strategies address the tradeoff between HO failures 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 open radio access network (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 an anonymized real-world dataset and a synthetic dataset obtained with Keysight’s EXata emulation tool. The results show the models’ ability to capture the dynamic and graph-structured nature of cellular networks. Finally, we present key considerations for real-world deployment that outline future steps to enable the integration of GNN-based link prediction for mobility management in O-RAN networks.