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Maximizing constructive interference based on orthogonality and diversity in MISO-VLC systems using FSTD-MRC algorithms

  • Mountadher Essa ORCID logo and Adnan Sabbar ORCID logo EMAIL logo
Published/Copyright: May 23, 2025
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Abstract

Spatial diversity in Visible Light Communication (VLC) systems serves as a robust technique for enhancing system performance by utilizing multiple light sources at the transmitter. Meanwhile, the concept of transmitting data orthogonally is employed to facilitate the separation of overlapping optical signals at the photodetector. Therefore, this paper presents a modification for the Frequency Switching Transmit Diversity (FSTD) technique to achieve a higher diversity order while preserving its orthogonality property. As a result of the increased diversity order, Maximal Ratio Combining (MRC) technology is suggested at the receiver to effectively combine received signals; this technique maximizes constructive interference while minimizing the impact of signals degraded by attenuation and obstructions. Additionally, MRC mitigates the effects of unwanted radiation, such as sunlight and ambient light. On this basis, the results demonstrate that the proposed approach achieved a Bit Error Rate (BER) of 3.4×10−5 at a transmission distance of 40 m with a Signal to Noise Ratio (SNR) of 5 dB. In comparison, the conventional FSTD technique recorded a BER of 1×10−3 under the same conditions. These findings highlight the superiority of the proposed approach in enhancing the efficiency and reliability of VLC systems, effectively mitigating the impact of optical channel blockages.


Corresponding author: Adnan Sabbar, Electrical Engineering Department, College of Engineering, University of Kufa, Najaf, Iraq; and Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Najaf, Iraq, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has 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 author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2025-04-14
Accepted: 2025-04-29
Published Online: 2025-05-23

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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