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Li, Juncheng; Huang, Yifan; Huang, Shenjie; Tavakkolnia, Iman; Haas, Harald; Safari, Majid
Integrated Communication and Positioning for IRS-Assisted LiFi Networks: 2024 IEEE Wireless Communications and Networking Conference (WCNC) Journal Article
In: 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings, pp. 01–06, 2024, ISSN: 979-8-3503-0359-9, (Place: Piscataway, NJ).
Abstract | Links | BibTeX | Tags: Downlink, Light fidelity, machine learning algorithms, mobile communication, Optical fiber networks, surface emitting lasers, Wireless communication
@article{li_integrated_2024,
title = {Integrated Communication and Positioning for IRS-Assisted LiFi Networks: 2024 IEEE Wireless Communications and Networking Conference (WCNC)},
author = {Juncheng Li and Yifan Huang and Shenjie Huang and Iman Tavakkolnia and Harald Haas and Majid Safari},
url = {https://ieeexplore.ieee.org/document/10570819/},
doi = {10.1109/wcnc57260.2024.10570819},
issn = {979-8-3503-0359-9},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-20},
booktitle = {2024 IEEE Wireless Communications and Networking Conference (WCNC)},
journal = {2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings},
pages = {01–06},
publisher = {IEEE},
address = {Dubai, United Arab Emirates},
series = {IEEE Wireless Communications and Networking Conference, WCNC},
abstract = {Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution to achieve high-speed mobile communications. To address the misalignment challenges encountered in laser-based LiFi, this study introduces an innovative full-coverage indoor LiFi system with integrated communication and positioning capabilities, leveraging intelligent reflected surfaces (IRSs). By design, the proposed system ensures successful wireless downlink connectivity, irrespective of the user's random location and orientation status. An algorithm is developed to ascertain the optimal deployment of both access points (APs) and IRS layers. Moreover, this study introduces a machine learning (ML)-based OWC positioning approach designed to enhance the accuracy of the user positioning, thereby effectively boosting the performance of the IRS-assisted communication system. Numerical results demonstrate the superiority of the proposed positioning approach over traditional methods in terms of average data rate.},
note = {Place: Piscataway, NJ},
keywords = {Downlink, Light fidelity, machine learning algorithms, mobile communication, Optical fiber networks, surface emitting lasers, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution to achieve high-speed mobile communications. To address the misalignment challenges encountered in laser-based LiFi, this study introduces an innovative full-coverage indoor LiFi system with integrated communication and positioning capabilities, leveraging intelligent reflected surfaces (IRSs). By design, the proposed system ensures successful wireless downlink connectivity, irrespective of the user's random location and orientation status. An algorithm is developed to ascertain the optimal deployment of both access points (APs) and IRS layers. Moreover, this study introduces a machine learning (ML)-based OWC positioning approach designed to enhance the accuracy of the user positioning, thereby effectively boosting the performance of the IRS-assisted communication system. Numerical results demonstrate the superiority of the proposed positioning approach over traditional methods in terms of average data rate.