BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models
This addresses beam prediction challenges in mmWave communication systems, particularly for vehicle-to-infrastructure scenarios, representing an incremental improvement by combining existing methods.
The paper tackles the problem of high training overhead and latency in mmWave communication systems by proposing BeamLLM, a vision-aided beam prediction framework using large language models, achieving 61.01% top-1 and 97.39% top-3 accuracy in standard tasks and showing limited degradation in few-shot scenarios.
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.