A Graph Foundation Model for Wireless Resource Allocation

• URL: http://arxiv.org/abs/2604.07390v1
• Date: Wed, 08 Apr 2026 06:07:28 GMT
• Title: A Graph Foundation Model for Wireless Resource Allocation
• Abstract summary: We propose a graph foundation model for resource allocation based on a pre-training and fine-tuning paradigm to extract unified representations.<n>Specifically, we introduce an interference-aware Transformer architecture with a bias projector that injects interference topologies into global attention mechanisms.<n>Experiments demonstrate that the proposed framework achieves state-of-the-art performance and scales effectively with increased model capacity.
• Score: 37.503398874234094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aggressive densification of modern wireless networks necessitates judicious resource allocation to mitigate severe mutual interference. However, classical iterative algorithms remain computationally prohibitive for real-time applications requiring rapid responsiveness. While recent deep learning-based methods show promise, they typically function as task-specific solvers lacking the flexibility to adapt to different objectives and scenarios without expensive retraining. To address these limitations, we propose a graph foundation model for resource allocation (GFM-RA) based on a pre-training and fine-tuning paradigm to extract unified representations, thereby enabling rapid adaptation to different objectives and scenarios. Specifically, we introduce an interference-aware Transformer architecture with a bias projector that injects interference topologies into global attention mechanisms. Furthermore, we develop a hybrid self-supervised pre-training strategy that synergizes masked edge prediction with negative-free Teacher-Student contrastive learning, enabling the model to capture transferable structural representations from massive unlabeled datasets. Extensive experiments demonstrate that the proposed framework achieves state-of-the-art performance and scales effectively with increased model capacity. Crucially, leveraging its unified representations, the foundation model exhibits exceptional sample efficiency, enabling robust few-shot adaptation to diverse and unsupervised downstream objectives in out-of-distribution (OOD) scenarios. These results demonstrate the promise of pre-trained foundation models for adaptable wireless resource allocation and provide a strong foundation for future research on generalizable learning-based wireless optimization.

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