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Analysing the efficiency of optical system for visible light communication for different modulation schemes

  • Aziz Nanthaamornphong ORCID logo EMAIL logo
Published/Copyright: August 11, 2025
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Abstract

This paper presents a comprehensive analysis of the efficiency of optical systems for visible light communication (VLC) employing high-order modulation schemes such as 256- quadrature amplitude modulation (QAM) and 512-QAM. It evaluates and compares four advanced optical waveforms – orthogonal frequency division multiplexing (OFDM), filter bank multicarrier (FBMC), universal filtered multicarrier (UFMC), and non-orthogonal multiple access (NOMA) – in terms of bit error rate (BER) and power spectral density (PSD) performance under realistic VLC channel conditions. Simulation results reveal that at a BER of 10−3, optical NOMA achieves up to a 6 decibel (dB) signal-to-noise ratio (SNR) gain over OFDM, while UFMC and FBMC demonstrate gains of approximately 4 dB and 2 dB, respectively, illustrating their robustness to noise and suitability for high data-rate applications. PSD analysis shows that Optical NOMA achieves the most compact spectral distribution with power levels near −350 W/MHz, significantly reducing out-of-band emissions compared to OFDM’s −200 W/MHz. This underscores the superior spectral efficiency and interference mitigation capabilities of these advanced schemes. The findings highlight that adopting FBMC, UFMC, and especially NOMA in VLC systems can dramatically enhance throughput and spectral utilization, making them highly promising for next-generation optical wireless networks that demand high capacity, low interference, and dense deployment scenarios.


Corresponding author: Aziz Nanthaamornphong, College of Computing, Prince of Songkla University, Phuket, Thailand, 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: Not applicable.

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

  6. Research funding: Not applicable.

  7. Data availability: Not applicable.

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Received: 2025-07-16
Accepted: 2025-07-26
Published Online: 2025-08-11

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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