Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection

TL;DR

This paper introduces a novel mmWave beam selection method that leverages environment semantics extracted from camera images to improve accuracy and efficiency in wireless communication systems.

Contribution

It proposes a semantic-based beam selection approach using keypoint detection from images, explicitly modeling environmental features for better performance.

Findings

Outperforms existing vision-based methods in scatterer localization

Reduces computational and storage requirements compared to prior approaches

Achieves precise environment understanding for improved beam selection

Abstract

In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images. Specifically, we first define the environment semantics as the spatial distribution of the scatterers that affect the wireless propagation channels and utilize the keypoint detection technique to extract them from the input images. Then, we design a deep neural network with the environment semantics as the input that can output the optimal beam pairs at the mobile station (MS) and the base station (BS). Compared with the existing beam selection approaches that directly use images as the input, the proposed semantic-based method can explicitly obtain the environmental features that account for the propagation of wireless signals, thus reducing the storage and computational burden. Simulation results show that the proposed method can precisely estimate the location of the scatterers and outperform the existing works based on computer vision or light detection and ranging (LIDAR).