Graph Neural Networks in Large Scale Wireless Communication Networks: Scalability Across Random Geometric Graphs
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916982777970688 |
|---|---|
| author | Camargo, Romina Garcia Wang, Zhiyang Ribeiro, Alejandro |
| author_facet | Camargo, Romina Garcia Wang, Zhiyang Ribeiro, Alejandro |
| contents | The growing complexity of wireless systems has accelerated the move from traditional methods to learning-based solutions. Graph Neural Networks (GNNs) are especially well-suited here, since wireless networks can be naturally represented as graphs. A key property of GNNs is transferability: models trained on one graph often generalize to much larger graphs with little performance loss. While empirical studies have shown that GNN-based wireless policies transfer effectively, existing theoretical guarantees do not capture this phenomenon. Most works focus on dense graphs where node degrees scale with network size, an assumption that fails in wireless systems. In this work, we provide a formal theoretical foundation for transferability on Random Geometric Graphs (RGGs), a sparse and widely used model of wireless networks. We further validate our results through numerical experiments on power allocation, a fundamental resource management task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00896 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks in Large Scale Wireless Communication Networks: Scalability Across Random Geometric Graphs Camargo, Romina Garcia Wang, Zhiyang Ribeiro, Alejandro Signal Processing The growing complexity of wireless systems has accelerated the move from traditional methods to learning-based solutions. Graph Neural Networks (GNNs) are especially well-suited here, since wireless networks can be naturally represented as graphs. A key property of GNNs is transferability: models trained on one graph often generalize to much larger graphs with little performance loss. While empirical studies have shown that GNN-based wireless policies transfer effectively, existing theoretical guarantees do not capture this phenomenon. Most works focus on dense graphs where node degrees scale with network size, an assumption that fails in wireless systems. In this work, we provide a formal theoretical foundation for transferability on Random Geometric Graphs (RGGs), a sparse and widely used model of wireless networks. We further validate our results through numerical experiments on power allocation, a fundamental resource management task. |
| title | Graph Neural Networks in Large Scale Wireless Communication Networks: Scalability Across Random Geometric Graphs |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.00896 |