A Novel Beam Prediction Scheme Based on Multimodal Data with High Robustness

With the advancement of the Internet of Vehicles, accurate beam prediction is crucial for maintaining stable and high-quality wireless communication in dynamic environments. A large number of beam prediction schemes have been proposed, which can be broadly categorized into two types: beam prediction schemes based on channel state and side information. However, the beam prediction schemes based on channel state require significant training overhead and computational complexity. In addition, most schemes based on side information neither make effective use of multimodal data (such as camera, lidar, and position), nor consider the impact of environmental noise on prediction accuracy. To address these weaknesses, we propose a novel beam prediction scheme with high robustness based on the multimodal data. Specifically, the scheme first uses a variety of data augmentation methods to reduce the noise interference caused by the adverse environment. Then, we employ ResNet to map heterogeneous data into a unified linear space to achieve effective feature alignment and correspondence. Finally, we exploit the distinctive multi-head attention mechanism of the Transformer model to guarantee that the fused features are both representative and informative. The comparison of numerous numerical results demonstrates that the proposed scheme offers both high robustness and accuracy across various scenarios.