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.

介绍了一种新兴的计算机视觉和无线通信交叉的范例，利用机器学习和非射频数据，展示了计算机视觉如何增强视觉辅助的无线网络，提高可靠性且不损失频谱效率，同时指出RF感应和成像在鲁棒性方面的作用，并探讨了将RF和非RF模态融合来实现超可靠通信和智能6G网络所需的重要性。

无线通信与计算机视觉在5G以后的应用