Startseite Wavelength division multiplexed fiber systems performance analysis through optisystem simulation configuration based on multi pumped all optical amplifiers
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Wavelength division multiplexed fiber systems performance analysis through optisystem simulation configuration based on multi pumped all optical amplifiers

  • Ramachandran Thandaiah Prabu EMAIL logo , Jayabalan Vinothkumar , Arul Albert Isaac , Alagar Manavalan Balamurugan , Ata Kishore Kumar , Palani Karthikeyan und Marian Habbib Adel EMAIL logo
Veröffentlicht/Copyright: 5. September 2025
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Abstract

This paper has demonstrated the wavelength division multiplexed fiber systems performance analysis through the optisystem simulation configuration based on multi pumped all optical amplifiers. Raman amplification gain, amplifier noise figure and optical output power are measured against different pump amplification levels and single mode fiber length at different spectral operating wavelengths. Either data rate per channel or per link is clarified versus the input signal power with multipumped Raman amplifiers (Seven pumps) and without Raman amplification. As well as the data rat fiber capacity product per channel or per link is indicated in relation to the optimum input signal power with seven multipumped Raman amplifiers. Overall system Q factor, bit error rates and output power at receiver side are measured clearly against various amplification pump levels (Five/seven pumps) in the presence and absence of dispersion compensated fiber compensation.


Corresponding authors: Ramachandran Thandaiah Prabu, Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Saveetha University, Chennai, Tamilnadu, India, E-mail: ; and Marian Habbib Adel, South Institutue of Computer Science, Giza, 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: None declared.

  7. Data availability: Not applicable.

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Received: 2025-06-21
Accepted: 2025-08-04
Published Online: 2025-09-05

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 21.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/joc-2025-0254/html
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