Accurately aligning millimeter-wave (mmWave) and terahertz (THz) narrow beams is essential to satisfy reliability and high data rates of 5G and beyond wireless communication systems. However, achieving this objective is difficult, especially in vehicle-to-vehicle (V2V) communication scenarios, where both transmitter and receiver are constantly mobile. Recently, additional sensing modalities, such as visual sensors, have attracted significant interest due to their capability to provide accurate information about the wireless environment. To that end, in this paper, we develop a deep learning solution for V2V scenarios to predict future beams using images from a 360 camera attached to the vehicle. The developed solution is evaluated on a real-world multi-modal mmWave V2V communication dataset comprising co-existing 360 camera and mmWave beam training data. The proposed vision-aided solution achieves $\approx 85\%$ top-5 beam prediction accuracy while significantly reducing the beam training overhead. This highlights the potential of utilizing vision for enabling highly-mobile V2V communications.

翻译：精确对准毫米波（mmWave）和太赫兹（THz）窄波束对于满足5G及未来无线通信系统的可靠性与高数据速率至关重要。然而，这一目标在车对车（V2V）通信场景中尤为困难，因为发射机和接收机均处于持续移动状态。近年来，视觉传感器等附加感知模态因其能够提供精确的无线环境信息而备受关注。为此，本文提出一种用于V2V场景的深度学习解决方案，利用车载360度摄像头拍摄的图像预测未来波束。该方案基于包含同步360度摄像头与毫米波波束训练数据的真实多模态V2V通信数据集进行评估。所提出的视觉辅助方案在显著降低波束训练开销的同时，实现了约85%的前五波束预测准确率。这一成果凸显了利用视觉技术赋能高移动性V2V通信的潜力。