Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks

• URL: http://arxiv.org/abs/2006.09902v2
• Date: Thu, 18 Jun 2020 03:09:15 GMT
• Title: Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
• Authors: Gouranga Charan, Muhammad Alrabeiah, and Ahmed Alkhateeb
• Abstract summary: This paper proposes a novel solution that proactively predicts textitdynamic link blockages. It learns from observed sequences of RGB images and beamforming vectors how to predict possible future link 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.
• Score: 11.626009272815816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \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.

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