AMBER: An Adaptive Multimodal Mask Transformer for Beam Prediction with Missing Modalities

The article introduces AMBER, a novel approach using a multimodal mask transformer for beam prediction, specifically addressing scenarios with missing modalities. This suggests a focus on robustness and adaptability in handling incomplete data, which is a significant challenge in multimodal AI. The use of a transformer architecture indicates a potential for capturing complex relationships between different modalities. The research likely explores the performance of AMBER compared to existing methods in terms of accuracy and efficiency, particularly when dealing with missing data.