Sensing-Assisted High Reliable Communication: A Transformer-Based Beamforming Approach
Yuanhao Cui, Jiali Nie, Xiaowen Cao, Tiankuo Yu, Jiaqi Zou, Junsheng Mu, Xiaojun Jing
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.