Vision-Aided Radio: User Identity Match in Radio and Video Domains Using Machine Learning

TL;DR

This paper presents a machine learning framework that matches user identities across radio and video data domains, enhancing environmental awareness in 5G networks with high accuracy.

Contribution

It introduces a novel framework for matching user identities in radio and visual data, addressing a gap in current communication network capabilities.

Findings

Deep Neural Network outperformed Random Forest in accuracy.

Achieved over 99% classification accuracy.

Validated framework across different environments.

Abstract

5G is designed to be an essential enabler and a leading infrastructure provider in the communication technology industry by supporting the demand for the growing data traffic and a variety of services with distinct requirements. The use of deep learning and computer vision tools has the means to increase the environmental awareness of the network with information from visual data. Information extracted via computer vision tools such as user position, movement direction, and speed can be promptly available for the network. However, the network must have a mechanism to match the identity of a user in both visual and radio systems. This mechanism is absent in the present literature. Therefore, we propose a framework to match the information from both visual and radio domains. This is an essential step to practical applications of computer vision tools in communications. We detail the proposed framework training and deployment phases for a presented setup. We carried out practical experiments using data collected in different types of environments. The work compares the use of Deep Neural Network and Random Forest classifiers and shows that the former performed better across all experiments, achieving classification accuracy greater than 99%.