Home Technology Performance analysis of 10 Gbps underwater optical wireless communication system using NRZ, duobinary, and AMI modulation under diverse water types
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Performance analysis of 10 Gbps underwater optical wireless communication system using NRZ, duobinary, and AMI modulation under diverse water types

  • Gunasekaran Thangavel EMAIL logo , Ramasamy Ganeshmurthi , Amsaveni Manigandan , Maralawadi Venkatasubbaiah Kumudavalli and Muthukrishnan Vijaya Maheswari
Published/Copyright: December 30, 2025
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Abstract

Underwater optical wireless communication (UOWC) presents a promising solution for achieving high-speed underwater data transmission. This study investigates the performance of non-return-to-zero (NRZ), duobinary, and alternate mark inversion (AMI) modulation formats at 10 Gbps under diverse aquatic environments including pure sea (PS), clear ocean (CO), coastal ocean (CS), harbor I (HI), and harbor II (HII). Quantitative performance is evaluated in terms of Q factor, bit error rate (BER), and eye diagram analysis with increasing transmission range. For example, in PS water at 50 m, NRZ achieves a Q factor of 6.21 dB and BER of 10−9.6, outperforming duobinary (Q = 4.79 dB, BER = 10−6.8) and AMI (Q = 3.69 dB, BER = 10−3.96). Across all ranges and water types, NRZ consistently provides the best performance, while duobinary offers intermediate results and AMI shows the highest degradation in turbid waters such as harbor I and harbor II. The findings highlight NRZ’s robustness for high-speed UOWC links, particularly in clear waters, and its relative advantage over duobinary and AMI in maintaining reliability over extended distances.

Keywords: UOWC; AMI; NRZ; duobinary; Q factor; BER

Corresponding author: Gunasekaran Thangavel, Engineering Department, College of Engineering and Technology, University of Technology and Applied Sciences, Muscat, Oman, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Gunasekaran Thangavel: Methodology, Resources, Writing – original draft. R. Ganeshmurthi: Software, Writing – review & editing. Amsaveni Manigandan: Investigation, Data Collection and analysis, Writing – review & editing. M. V. Kumudavalli: Conceptualization, Writing – review & editing. M. Vijaya Maheswari: Project Administration, Writing – review & editing.

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

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

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-10-10
Accepted: 2025-11-26
Published Online: 2025-12-30

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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