Experimental 5G New Radio integration with VLC

International audienceIn this paper, integration of 5G New Radio (5G NR) with a Visible Light Communication (VLC) downlink architecture is proposed. This scheme combines two complementary wireless technologies: upcoming 5G NR and VLC to offer indoor enhanced wireless hybrid access able to provide each User Equipment (UE) with very high data rate and positioning support. The data transmission of the 5G NR frame over VLC has been implemented. This represents a novel approach to transmitting 5G NR over VLC by hardware experimentation based on Universal Software Radio Peripheral (USRP). The experiment results show that the proposed scheme with Quadrature Phase Shift Keying (QPSK) mapping achieves a data rate of 14.4 M bits/s and an Error Vector Magnitude (EVM) of 4.78% for a 55 cm free space transmission span

Experimental measurements of a joint 5G-VLC communication for future vehicular networks

One of the main revolutionary features of 5G networks is the ultra-low latency that will enable new services such as those for the future smart vehicles. The 5G technology will be able to support extreme-low latency. Thanks to new technologies and the wide flexible architecture that integrates new spectra and access technologies. In particular, Visible Light Communication (VLC) is envisaged as a very promising technology for vehicular communications, since the information can flow by using the lights (as traffic-lights and car lights). This paper describes one of the first experiments on the joint use of 5G and VLC networks to provide real-time information to cars. The applications span from road safety to emergency alarm

On improving 5G internet of radio light security based on led fingerprint identification method

Copyright: © 2021 by the authors. In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%.EU Horizon 2020 program towards the Internet of Radio-Light project H2020-ICT 761992