Meeting the high data rate demands of modern applications necessitates the utilization of high-frequency spectrum bands, including millimeter-wave and sub-terahertz bands. However, these frequencies require precise alignment of narrow communication beams between transmitters and receivers, typically resulting in significant beam training overhead. This paper introduces a novel end-to-end vision-aided beamforming framework that utilizes images to predict optimal beams while considering geometric adjustments to reduce overhead. Our model demonstrates robust adaptability to dynamic environments without relying on additional training data where the experimental results indicate a top-5 beam prediction accuracy of 98.96%, significantly surpassing current state-of-the-art solutions in vision-aided beamforming.

翻译：为满足现代应用对高数据速率的需求，必须利用高频频谱资源，包括毫米波与亚太赫兹频段。然而，这些频段要求发射端与接收端之间实现窄波束的精确对准，通常会导致显著的波束训练开销。本文提出了一种新颖的端到端视觉辅助波束成形框架，该框架利用图像预测最优波束，同时考虑几何调整以降低开销。我们的模型展现出对动态环境的强适应能力，且无需依赖额外的训练数据。实验结果表明，该模型在Top-5波束预测准确率上达到98.96%，显著超越了当前视觉辅助波束成形领域的最先进解决方案。