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Ultra high tera bit capacity reach through multicore fibers based on high power efficient transmission spatial division multiplexing technology for advanced optical fiber communication links

  • Govindaraj Ramkumar EMAIL logo , Soman Shibu , Ramachandran Ashwini , Pattabhirama Mohan Patnala , Mandyam Venkatanaresh , Sajiv George and Wafaa Fahim Hossam Zain EMAIL logo
Published/Copyright: June 30, 2025
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Abstract

This paper has clarified the ultrahigh terabit capacity reach through multicore fibers based on high power efficient transmission spatial division multiplexing technology for advanced optical fiber communication links. The system spectral efficiency is measured against signal to noise ratio variations in the presence of various modulation techniques. These modulation techniques are namely that differential phase shift keying (DPSK), quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM). Channel loss into the fiber link per core fiber is demonstrated versus the spectral operating wavelength variations with different core fibers. The forward error correction is clarified versus both number of transmitted channel per fiber link and data transmission bit rates. The spectral system efficiency is indicated in relation to both number of transmitted channel per fiber link and data transmission bit rates. Total SDM multiplexing system bit rate and the total SDM multiplexing system distance capacity product are clarified against the number of fiber core and number of links per fiber core at various modulation techniques.


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 Wafaa Fahim Hossam Zain, Sakrah Institute of Communication Technology, Sinia, 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: 2025-05-17
Accepted: 2025-06-12
Published Online: 2025-06-30

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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