Sensing-Assisted High Reliable Communication: A Transformer-Based Beamforming Approach

Abstract

Beamforming improves the received signal power and eliminates undesirable interference by sharpening the transmitted signal toward a specific direction, enhancing service quality in the future vehicle network. However, the traditional beam codebook has gradually failed to cope with high-speed mobile services and complex pavement conditions due to beam misalignment and channel fading. To address the challenges above, this paper proposes a transformer-based beamforming approach to achieve sensing-assisted high reliable communication. We use the multimodal data collected by the sensors at the base station for beamforming to optimize the communication performance. The proposed model employs three-dimensional (3D) ResNet-18 to extract multimodal features and leverages the transformer's merged-attention mechanism to fuse these features for beamforming. The experimental result based on real-world vision, radar, LiDAR, and position data shows the advance of our proposed method, which achieves 91.59% top-3 accuracy on average and exceeds over 30% top-1 accuracy than single-modal schemes in the high-speed environment.