This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications). First, drawing on recent advances in machine learning and the availability of non-RF data, vision-aided wireless networks are shown to significantly enhance the reliability of wireless communication without sacrificing spectral efficiency. In particular, we demonstrate how computer vision enables {look-ahead} prediction in a millimeter-wave channel blockage scenario, before the blockage actually happens. From a computer vision perspective, we highlight how radio frequency (RF) based sensing and imaging are instrumental in robustifying computer vision applications against occlusion and failure. This is corroborated via an RF-based image reconstruction use case, showcasing a receiver-side image failure correction resulting in reduced retransmission and latency. Taken together, this article sheds light on the much-needed convergence of RF and non-RF modalities to enable ultra-reliable communication and truly intelligent 6G networks.
翻译:本文阐述了新兴的范式,即坐在计算机视觉和无线通信的交汇处,使5G/6G任务关键应用(自动/遥控车辆、超光速VR和其他网络物理应用)能够超越5G/6G任务关键应用(自动/遥控车辆、超光速VR和其他网络物理应用)。首先,利用机器学习的最新进展和非RF数据的可用性,视觉辅助无线网络显示在不牺牲光谱效率的情况下大大提高了无线通信的可靠性。特别是,我们展示了计算机视觉如何在阻塞实际发生之前,在毫米波频道阻塞情景中进行{视线}预测。我们从计算机视觉角度强调无线电频率(RF)的遥感和成像如何有助于加强计算机视觉应用,防止隐蔽和故障。这通过基于RF图像重建的图像使用案例得到证实,展示了接收器侧图像失灵的纠正,从而减少了再传输和隐蔽性。这篇文章共同揭示了非常需要的RF和非R模式汇合在一起,以便实现极易通信和真正智能的6G网络。