@inproceedings{e207a009280941f492a23543f504dadc,
title = "Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks",
abstract = "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 dynamic link blockages. More specifically, it develops a deep neural network architecture that learns from observed sequences of RGB images and beam-forming 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.",
keywords = "blockage prediction, computer vision, Deep learning, mmWave, terahertz",
author = "Gouranga Charan and Muhammad Alrabeiah and Ahmed Alkhateeb",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 ; Conference date: 14-06-2021 Through 23-06-2021",
year = "2021",
month = jun,
doi = "10.1109/ICCWorkshops50388.2021.9473651",
language = "English (US)",
series = "2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings",
}