Journal
SENSORS
Volume 21, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/s21041515
Keywords
visible light communication; LED fingerprint; 5G networks; security
Funding
- EU Horizon 2020 program [H2020-ICT 761992]
Ask authors/readers for more resources
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.
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%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available