Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

出现了各种智能连接设备，例如自动驾驶汽车，自动机器人和遥控建筑车辆。这些设备具有视觉系统，可以无碰撞地进行操作。得益于深度学习技术的巨大进步，机器视觉技术越来越易于​​感知自我位置和/或周围环境。这些智能连接设备的准确感知信息使预测无线链路质量（LQ）成为可能。本文提出了一种将机器学习应用于高清摄像机输出的LQ预测方案，以预测周围移动物体对LQ的影响。所提出的方案利用基于深度学习的对象检测，并学习检测到的对象位置信息和LQ之间的关系。