A multimodal graph learning method for V2X beam alignment cuts overhead by over 90% and outperforms prior federated learning baselines under label and modality imbalance.
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis
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Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach
A multimodal graph learning method for V2X beam alignment cuts overhead by over 90% and outperforms prior federated learning baselines under label and modality imbalance.