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Nonlinear FWM effects management and FWM system performance analysis in ultra wide optical wavelength division multiplexing systems

  • Govindaraj Ramkumar EMAIL logo , Govindanaidu Damodaran Vignesh , Manimaran Arjunan , Priscilla Mohanadoss , Ramachandran Vidhya Muthulakshmi , Mandyam Venkatanaresh and Ayman Zain Hedaman EMAIL logo
Published/Copyright: June 16, 2025
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Abstract

This paper has highlighted the nonlinear FWM (fourwave mixing) effects management and FWM system performance analysis in ultra wide optical wavelength division multiplexing systems. The optical output power is measured with the fiber propagation length based on first/second signals and first/second induced signals. The FWM output power is demonstrated with channel spacing, dispersion coefficient and input signal power based on various modulation techniques. FWM system signal per noise ratio and BER are clarified with the dispersion coefficient with different modulation techniques in FWM system. Maximum transmit power per channel is investigated with the propagation fiber length with different modulation techniques. Maximum mixing efficiency is measured with the fiber dispersion coefficients and channel spacing. Normalized conversion efficiency is demonstrated against the operating wavelength. The FWM power penalty is analyzed and measured against input signal power and total fiber dispersion coefficient. Total system bit rate and total system disperstion are clarified in relation to input signal power with/without chirped FBG with NRZ-PSK modulation technique.


Corresponding authors: Govindaraj Ramkumar, Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Saveetha University, Chennai, Tamil Nadu, India, E-mail: ; and Ayman Zain Hedaman, Ismaillia Institute of Technology, Ismaillia, Egypt, 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: Not applicable.

  7. Data availability: Not applicable.

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Received: 2025-04-21
Accepted: 2025-05-15
Published Online: 2025-06-16

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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