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Performance evaluation of zero cross correlation code (ZCC code) based passive optical network

  • Gurpreet Kaur ORCID logo EMAIL logo , Ramandeep Kaur , Rajandeep Singh ORCID logo , Rajneesh Randhawa and Rohan Kumar
Published/Copyright: February 2, 2026
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Abstract

In this paper, a zero cross-correlation code (ZCC)–based optical code division multiple access passive optical network (OCDMA-PON) is presented. The proposed ZCC assigns a unique and non-overlapping set of wavelengths to each optical network unit (ONU), ensuring zero cross-correlation among users. Unlike conventional codes such as multi-diagonal (MD) or modified quadratic congruence (MQC), the proposed ZCC offers a simpler recursive construction, flexible scalability, and complete elimination of multiple access interference. An eight-user ZCC-based PON system is designed and its performance is evaluated in terms of bit error rate (BER) and Q-factor. Simulation results show that the proposed system achieves a BER below 10−15 and a Q-factor of approximately 9 dB at a transmission distance of 10 km, demonstrating improved interference suppression and reliable performance compared to previously reported OCDMA schemes. These results confirm the suitability of ZCC for secure and efficient next-generation optical access networks.

Keywords: ZCC; OCDMA; PON; BER; Q-factor

Corresponding author: Gurpreet Kaur, Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147004, Punjab, India, E-mail:

Acknowledgments

We wish to thank the APON Lab of Punjabi University, Patiala, and Guru Nanak Dev University (GNDU), Amritsar, for providing the computational system and support.

  1. Research ethics: This study complies with the ethical standards of engineering and scientific research. No human participants or animal subjects were involved, and no Ethical Approval was required.

  2. Informed consent: Not applicable.

  3. Author contributions: All 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: The authors confirm that no Large Language Models (LLMs), AI, or machine learning tools were used in the writing, data analysis, or preparation of this manuscript.

  5. Conflict of interest: The authors declare that there are no known financial or personal conflicts of interest that could have appeared to influence the work reported in this paper.

  6. Research funding: Not applicable.

  7. Data availability: Not applicable.

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Received: 2025-10-30
Accepted: 2026-01-13
Published Online: 2026-02-02

© 2026 Walter de Gruyter GmbH, Berlin/Boston

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