Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach

TL;DR

This paper introduces a scalable, multimodal graph neural network framework for efficient beam alignment in V2X systems, significantly reducing overhead and improving robustness through innovative data augmentation and federated learning.

Contribution

It proposes a novel distributed multimodal graph learning approach with hybrid centralized and federated training, plus a data augmentation scheme to handle data imbalance in vehicular networks.

Findings

Reduces beam alignment overhead by over 90%.

Maintains competitive sum rate with high-resolution codebook feedback.

Outperforms state-of-the-art federated learning benchmarks, especially under data imbalance.

Abstract

Efficient beam alignment is fundamental to high-throughput and reliable connectivity in Vehicle-to-Everything (V2X) systems. However, conventional beam management in dynamic vehicular topologies incurs prohibitive alignment overhead and struggles to maintain robust links under rapid mobility. To overcome these challenges, this paper proposes a distributed multimodal graph beam alignment (GBA) framework. The core innovation lies in leveraging onboard multimodal sensing data to predict implicit feedback while employing graph neural networks to coordinate multi-user alignment, thereby jointly enhancing scalability and drastically reducing overhead. The architecture adopts a dual-network design with GBA-RSU and GBA-Vehicle units, optimized through a hybrid strategy of centralized learning and federated learning (FL) to balance global performance with local privacy. Furthermore, a dedicated data augmentation (DA) scheme is introduced to address multimodal data imbalance issues in vehicular networks. Negative augmentation applies dominant modality dropout to bolster robustness, while positive augmentation generates underrepresented samples to mitigate label imbalance. Numerical results demonstrate that GBA maintains a competitive sum rate on par with high-resolution codebook-based feedback yet reduces beam alignment overhead by over 90\% and scales efficiently in mobile scenarios. Notably, integrating DA enables GBA to consistently outperform state-of-the-art FL-based alignment benchmarks, with particularly pronounced gains under severe label and modality imbalance, establishing a practical solution for V2X beam management.