Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915520408715264 |
|---|---|
| author | Bermudez, Ana Gonzalez Farreras, Miquel Groshev, Milan Trujillo, José Antonio de la Bandera, Isabel Barco, Raquel |
| author_facet | Bermudez, Ana Gonzalez Farreras, Miquel Groshev, Milan Trujillo, José Antonio de la Bandera, Isabel Barco, Raquel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach Bermudez, Ana Gonzalez Farreras, Miquel Groshev, Milan Trujillo, José Antonio de la Bandera, Isabel Barco, Raquel Networking and Internet Architecture Artificial Intelligence 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. |
| title | Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2502.02170 |