Deep Learning based Computer-vision for Enhanced Beamforming

TL;DR

This paper presents a vision-aided beamforming framework using deep learning to predict optimal communication beams from images, significantly reducing training overhead and achieving high accuracy in dynamic environments.

Contribution

It introduces a novel end-to-end deep learning model that leverages visual data for efficient beam prediction, outperforming existing methods in accuracy and adaptability.

Findings

Top-5 beam prediction accuracy of 98.96%

Robust performance in dynamic environments

Surpasses current state-of-the-art solutions

Abstract

Meeting the high data rate demands of modern applications necessitates the utilization of high-frequency spectrum bands, including millimeter-wave and sub-terahertz bands. However, these frequencies require precise alignment of narrow communication beams between transmitters and receivers, typically resulting in significant beam training overhead. This paper introduces a novel end-to-end vision-aided beamforming framework that utilizes images to predict optimal beams while considering geometric adjustments to reduce overhead. Our model demonstrates robust adaptability to dynamic environments without relying on additional training data where the experimental results indicate a top-5 beam prediction accuracy of 98.96%, significantly surpassing current state-of-the-art solutions in vision-aided beamforming.