Design and implementation of an integrated OWC and RF network slicing-based architecture over hybrid LiFi and 5G networks

Abstract

Radio frequency (RF) systems tend to become congested and overused due to the increasing number of users, devices and the multiple technologies involved in their deployment. This leads to the downgrading of quality of service (QoS) further caused by interference with different signals. Optical Wireless communications (OWC) are emerging as a feasible alternative as they offer unlicensed, interference-free spectrum by using the frequency range located in the visible and invisible light spectrum. Its applications can be found in various fields such as healthcare, education, finance and industry 4.0. Moreover, it enhances the security and privacy of communications. Nevertheless, the limited spectrum in OWC also requires optimised resource allocation to support the QoS of different applications or users whilst lacking established infrastructure to manage this. To address these challenges, this paper proposes a novel 5G-LiFi framework able to ensure QoS requirements by introducing network slicing in Light Fidelity (LiFi) networks integrated with 5G infrastructure. This paper has developed and deployed a 5G-LiFi architecture capable of providing network slicing capabilities over the LiFi segment of the hybrid network. It allows a full control over the network traffic and tailored, improved QoS capabilities. The proposed solution has been empirically validated and evaluated in a realistic testbed employing real-world LiFi and 5G network equipment, and yielded promising results in terms of bandwidth, delay, jitter and packet loss. This work concludes that the use of heterogeneous networks integrating OWC with RF is a suitable solution and it can lead to a better use and exploitation of the different spectrums, improving the QoS offered to end-users.

Publication DOI: https://doi.org/10.1007/s11276-024-03848-5
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
Aston University (General)
Funding Information: This work is funded partly by the European Commission under two projects: 6 G BRAINS: Bringing Reinforcement learning Into Radio Light Network for Massive Connections (Grant Agreement Number 101017226) and RIGOUROUS: secuRe desIGn and deplOyment of trUsth
Additional Information: Copyright © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Uncontrolled Keywords: LiFi,5G,IoT,network slicing,OWC,RF,QoS
Publication ISSN: 1572-8196
Last Modified: 18 Apr 2025 07:25
Date Deposited: 11 Apr 2025 14:03
Full Text Link:
Related URLs: https://link.sp ... 276-024-03848-5 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2025-02
Published Online Date: 2024-10-30
Accepted Date: 2024-09-05
Authors: Khadmaoui-Bichouna, Mohamed
Matencio Escolar, Antonio
Alcaraz-Calero, Jose M. (ORCID Profile 0000-0002-2654-7595)
Wang, Qi

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