基于视觉辅助的6G网络动态阻塞预测
Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
摘要：
释放毫米波和亚太赫兹无线通信网络的全部潜力取决于实现前所未有的低延迟和高可靠性要求。满足这些要求的挑战部分在于毫米波和亚太赫兹频率范围内的信号对阻塞的敏感性。解决这一挑战的一个有希望的方法是使用机器学习帮助无线网络发展对其周围环境的感知。本文试图通过利用深度学习和计算机视觉来做到这一点。它提出了一种新的解决方案，主动预测\textit{dynamic}链路阻塞。更具体地说，它开发了一种深度神经网络架构，该架构可以从观察到的RGB图像序列和波束成形向量中学习如何预测未来可能的链路阻塞。所提出的架构进行评估的公开可用的数据集，代表一个合成的动态通信场景与多个移动用户和阻塞。它在86%附近的链接阻塞预测准确度得分，如果不利用视觉数据，这种性能不太可能匹配。
英文摘要：
Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements. The challenge in meeting those requirements lies partly in the sensitivity of signals in the millimeter-wave and sub-terahertz frequency ranges to blockages. One promising way to tackle that challenge is to help a wireless network develop a sense of its surrounding using machine learning. This paper attempts to do that by utilizing deep learning and computer vision. It proposes a novel solution that proactively predicts \textit{dynamic} link blockages. More specifically, it develops a deep neural network architecture that learns from observed sequences of RGB images and beamforming vectors how to predict possible future link blockages. The proposed architecture is evaluated on a publicly available dataset that represents a synthetic dynamic communication scenario with multiple moving users and blockages. It scores a link-blockage prediction accuracy in the neighborhood of 86\%, a performance that is unlikely to be matched without utilizing visual data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
