Multi-modal sensing-assisted beam prediction using real-world dataset

This paper explores techniques for beam prediction using multi-modal sensing data. Specifically, our aim is to develop a deep learning model that predicts the optimal beam using information collected from camera, LiDAR, radar, and GPS sensors. For this purpose, we propose ResNet-SE, which integrates a squeeze-and-excitation network with ResNet, and PIformer, a transformer-based model we design using pooling layers and inception mixer modules. Experimental results demonstrate a 22% improvement in prediction accuracy and a 38% reduction in training time compared to the state-of-the-art model.