When Wireless Communications Meet Computer Vision in Beyond 5G
When Wireless Communications Meet Computer Vision in Beyond 5G
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DOI：
10.1109/mcomstd.001.2000047
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发表时间：
2020-10
影响因子：
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通讯作者：
T. Nishio;Yusuke Koda;Jihong Park;M. Bennis;K. Doppler
中科院分区：
文献类型：
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作者：
T. Nishio;Yusuke Koda;Jihong Park;M. Bennis;K. Doppler
本文阐述了新兴的范例，坐在计算机视觉和无线通信的交汇处，实现超越5G/6 G的关键任务应用（自主/远程控制车辆，视觉触觉虚拟现实和其他网络物理应用）。首先，利用机器学习的最新进展和非射频（RF）数据的可用性，视觉辅助无线网络已被证明可以在不牺牲频谱效率的情况下显着提高无线通信的可靠性。特别是，我们演示了计算机视觉如何在实际发生阻塞之前在毫米波信道阻塞情况下实现前瞻预测。从计算机视觉的角度来看，我们强调了基于RF的传感和成像如何有助于增强计算机视觉应用对遮挡和故障的鲁棒性。这一点通过基于RF的图像重建用例得到了证实，该用例展示了接收器侧图像故障校正，从而减少了重传和延迟。综上所述，本文揭示了迫切需要的RF和非RF模式的融合，以实现超可靠的通信和真正智能的6 G网络。
This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, enabling beyond-5G/6G mission-critical applications (autonomous/ remote-controlled vehicles, visuo-haptic virtual reality, and other cyber-physical applications). First, drawing on recent advances in machine learning and the availability of non-radio-frequen-cy (RF) data, vision-aided wireless networks have been shown to significantly enhance wireless communication reliability 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 occurs. From a computer vision perspective, we highlight how 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.