用视觉和大模型提升毫米波波束预测精度与效率
BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models
- 结合视觉与大模型跨模态推理，从图像中提取用户位置特征
- 在真实车路场景下，顶1准确率达61.01%，顶3达97.39%
- 少样本场景下性能下降小，适合实时性要求高的通信系统
本文提出BeamLLM，一种融合视觉与大语言模型（LLMs）的毫米波（mmWave）波束预测框架，旨在解决传统方法训练开销高、延迟大的问题。通过将计算机视觉（CV）与LLMs的跨模态推理能力结合，该框架从RGB图像中提取用户设备（UE）的位置特征，并利用重编程技术将视觉-时序特征对齐至LLM的语义空间。在真实的车路协同（V2I）场景下评估显示，该方法在标准预测任务中达到61.01%的顶1准确率和97.39%的顶3准确率，显著优于传统深度学习模型。在少样本预测场景中，从时间样本1到10，顶1准确率仅下降12.56%，顶3下降5.55%，展现出优异的预测能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。