论文标题
带相机的毫米波无人机：计算机视觉辅助无线光束预测
Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction
论文作者
论文摘要
毫米波（MMWave）和Terahertz（THZ）无人机有可能实现几种未来派应用程序，例如覆盖范围扩展，增强的安全监控和灾难管理。但是，这些无人机需要部署大型天线阵列并使用狭窄的指令梁来保持足够的链路预算。与这些阵列相关的大型横梁训练开销使这些狭窄的光束对高度移动的无人机有挑战性。为了应对这些挑战，本文提出了一种基于视觉的机器学习方法，该方法利用了从无人机上安装的相机收集的视觉数据，以实现快速准确的光束预测。此外，为了促进对拟议解决方案的评估，我们构建了一个由共存的无线和视觉数据组成的合成无人机通信数据集。拟议中的视觉辅助解决方案可实现$ \ 91 \％$的最高$ 1 $光束预测准确性，接近$ 100 \％$ $ top- $ 3 $精度。这些结果突出了所提出的解决方案在实现高度移动MMWAVE/THZ无人机通信方面的功效。
Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management. However, these drones need to deploy large antenna arrays and use narrow directive beams to maintain a sufficient link budget. The large beam training overhead associated with these arrays makes adjusting these narrow beams challenging for highly-mobile drones. To address these challenges, this paper proposes a vision-aided machine learning-based approach that leverages visual data collected from cameras installed on the drones to enable fast and accurate beam prediction. Further, to facilitate the evaluation of the proposed solution, we build a synthetic drone communication dataset consisting of co-existing wireless and visual data. The proposed vision-aided solution achieves a top-$1$ beam prediction accuracy of $\approx 91\%$ and close to $100\%$ top-$3$ accuracy. These results highlight the efficacy of the proposed solution towards enabling highly mobile mmWave/THz drone communication.
