BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models
Journal:
arXiv
Published Date:
Mar 13, 2025
Abstract
In this paper, we propose BeamLLM, a vision-aided millimeter-wave (mmWave)
beam prediction framework leveraging large language models (LLMs) to address
the challenges of high training overhead and latency in mmWave communication
systems. By combining 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, the proposed method achieves 61.01%
top-1 accuracy and 97.39% top-3 accuracy in standard prediction tasks,
significantly outperforming traditional deep learning models. In few-shot
prediction scenarios, the performance degradation is limited to 12.56% (top-1)
and 5.55% (top-3) from time sample 1 to 10, demonstrating superior prediction
capability.
