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

TL;DR

This paper presents a deep learning approach using visual data to proactively predict dynamic link blockages in 6G wireless networks, aiming to enhance reliability and low-latency communication.

Contribution

It introduces a novel neural network architecture that leverages RGB images and beamforming data for accurate blockage prediction in dynamic scenarios.

Findings

Achieves approximately 86% prediction accuracy.

Utilizes publicly available synthetic dataset for evaluation.

Demonstrates the effectiveness of vision-based prediction in wireless 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 \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.