AMBER: 一种自适应多模态掩码变换器用于缺失模态的波束预测
AMBER: An Adaptive Multimodal Mask Transformer for Beam Prediction with Missing Modalities
AI总结：
AMBER通过自适应处理多模态数据中的缺失模态问题，提升波束预测的鲁棒性和准确性，实验表明其在真实世界数据集上优于现有多模态学习基线。
摘要：
随着毫米波（mmWave）大规模多输入多输出（MIMO）在车载网络中的广泛应用，准确的波束预测和对齐已成为高速数据传输和可靠接入的关键。尽管传统波束预测方法主要依赖于带内波束训练，但近年来的研究开始探索多模态传感以提取环境语义以增强预测。然而，现有多模态融合方法在实际应用中性能显著下降，因为它们容易受到传感器阻挡、照明差或GPS断开导致的数据缺失影响。为解决这一挑战，我们提出AMBER（Adaptive Multimodal Mask Transformer for Beam Prediction），一种新颖的端到端框架，能够处理图像、LiDAR、雷达和GPS数据的时序序列，并自适应处理任意缺失模态的情况。AMBER引入了可学习的模态标记和缺失模态感知的掩码，以防止跨模态噪声传播，同时结合可学习的融合标记和多头注意力机制，实现稳健的模态特定信息蒸馏和特征级融合。此外，引入了类形式辅助的模态对齐（CMA）模块和时间感知的位置嵌入，以保持时间一致性并确保跨模态的语义对齐，从而学习到模态不变且时间一致的表示以进行波束预测。在真实世界DeepSense6G数据集上的广泛实验表明，AMBER显著优于现有的多模态学习基线。特别是，即使在严重的缺失模态场景下，它也能保持高波束预测准确性和鲁棒性，验证了其有效性和实际应用性。
英文摘要：
With the widespread adoption of millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) in vehicular networks, accurate beam prediction and alignment have become critical for high-speed data transmission and reliable access. While traditional beam prediction approaches primarily rely on in-band beam training, recent advances have started to explore multimodal sensing to extract environmental semantics for enhanced prediction. However, the performance of existing multimodal fusion methods degrades significantly in real-world settings because they are vulnerable to missing data caused by sensor blockage, poor lighting, or GPS dropouts. To address this challenge, we propose AMBER ({A}daptive multimodal {M}ask transformer for {BE}am p{R}ediction), a novel end-to-end framework that processes temporal sequences of image, LiDAR, radar, and GPS data, while adaptively handling arbitrary missing-modality cases. AMBER introduces learnable modality tokens and a missing-modality-aware mask to prevent cross-modal noise propagation, along with a learnable fusion token and multihead attention to achieve robust modality-specific information distillation and feature-level fusion. Furthermore, a class-former-aided modality alignment (CMA) module and temporal-aware positional embedding are incorporated to preserve temporal coherence and ensure semantic alignment across modalities, facilitating the learning of modality-invariant and temporally consistent representations for beam prediction. Extensive experiments on the real-world DeepSense6G dataset demonstrate that AMBER significantly outperforms existing multimodal learning baselines. In particular, it maintains high beam prediction accuracy and robustness even under severe missing-modality scenarios, validating its effectiveness and practical applicability.
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