Startseite Investigation and performance evaluation of the optical channel for visible light vehicular communication system employing orthogonal frequency division multiplexing
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Investigation and performance evaluation of the optical channel for visible light vehicular communication system employing orthogonal frequency division multiplexing

  • Sarmad R. Ramzi EMAIL logo , Atheer A. Sabri und Samir M. Hameed ORCID logo
Veröffentlicht/Copyright: 30. Juni 2025
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Abstract

The integration of intelligent transportation systems (ITS) has become a common approach for reducing traffic congestion and enhancing the safety of roadways and underground tunnels. Visible light communication (VLC) has emerged as a promising alternative for vehicular environments, offering unlicensed optical spectrum, high data rates, and immunity to radio frequency (RF) interference. This study examines the performance of an orthogonal frequency division multiplexing (OFDM)-based VLC system employing Bose–Chaudhuri–Hocquenghem (BCH) coding for error correction, aiming to explore the physical layer characteristics of VLC systems in a vehicular tunnel environment. By utilizing MATLAB as a simulation tool, the channel model was analyzed by incorporating the line of sight (LOS) and non-line of sight (NLOS) propagation models, dynamic speed, and various data rates. At a vehicle speed of around 50 km/h, the simulated scenario was capable of attaining a bit error rate (BER) of 3.5 × 10−5 when the data rate was 1.5 Mbps. However, for a data rate of 2.4 Mbps, the BER value was 0.18 %, with a significant increment when exhibiting high speed due to the instant change in the channel conditions. The results offer valuable insights into the feasibility of VLC in underground tunnels and highlight the critical trade-offs between data rate, mobility, and link reliability in dynamic vehicular environments.


Corresponding author: Sarmad R. Ramzi, College of Communication Engineering, University of Technology-Iraq, Baghdad, Iraq, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-04-30
Accepted: 2025-06-06
Published Online: 2025-06-30

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 8.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/joc-2025-0169/pdf
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