Vision-Aided Radio: User Identity Match in Radio and Video Domains Using Machine Learning
This addresses a critical bottleneck for integrating computer vision into communications, enabling practical applications like enhanced network awareness, though it is incremental as it builds on existing machine learning methods.
The paper tackles the problem of matching user identities between visual and radio domains in 5G networks, proposing a framework that achieves over 99% classification accuracy using a Deep Neural Network classifier.
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%.