BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models

In this paper, we propose BeamLLM, a vision-empowered millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to enhance the accuracy and robustness of beam prediction. By integrating computer vision (CV) with LLMs’ cross-modal reasoning capabilities, the framework extracts user equipment (UE) positional features from RGB images and aligns visual-temporal features with LLMs’ semantic space through reprogramming techniques. Evaluated on a realistic vehicle-to-infrastructure (V2I) scenario, BeamLLM achieves 61.01% top-1 accuracy and 97.39% top-3 accuracy in standard prediction tasks, outperforming traditional deep learning models. In few-shot prediction scenarios, performance degradation is limited to 12.56% (top-1) and 5.55% (top3) from time sample 1 to 10, demonstrating superior prediction capability.