On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method

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%.

Automated summary

This paper proposes a novel device identification method in 5G networks using LED fingerprints extraction, achieving up to 97.1% accuracy with the use of four machine-learning classifiers. The method has been theoretically investigated and experimentally verified, showing feasibility and accuracy in a practical VLC-based 5G network demonstration.