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Performance evaluation of GPON and XG-PON coexistence using CO-OFDM in fiber access networks

  • Taha I. Sabah ORCID logo EMAIL logo and Ghusoon M. Ali ORCID logo
Published/Copyright: September 8, 2025
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Abstract

This research investigates the coexistence of Gigabit Passive Optical Networks (GPON) and Next-Generation Passive Optical Network (XG-PON) for access networks. Advanced modulation Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM) with 4 Quadrature Amplitude Modulation (4-QAM) is used for the XG-PON downstream to get a 10 Gbps data rate, and Non-Return to Zero (NRZ) modulation format is used for the upstream to get 2.5 Gbps. In contrast, for the GPON system, the NRZ modulation format is used to achieve 2.5 Gbps and 1.25 Gbps for both downstream and upstream, respectively. The network employs a 1:32 split ratio, accommodating 31 GPON users and one XG-PON user over a 20 km distance. To evaluate GPON performance, eye diagrams, Q-factor vs. distance, and received power vs. Log Bit Error Rate (BER) metrics were examined in both downstream and upstream. For XG-PON, constellation diagrams, optical signal-to-noise ratio (OSNR) vs. log BER, error vector magnitude (EVM) vs. power receiver, and power transmission vs. log BER were used to measure system performance for CO-OFDM downstream. Eye diagrams, Q-factor, and received power vs. Log BER estimation were employed for upstream analysis. Results demonstrate that the proposed system achieves robust performance and effective coexistence between GPON and XG-PON, ensuring high-quality service delivery across both technologies.


Corresponding author: Taha I. Sabah, Electrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq, E-mail:

Acknowledgments

The authors would like to sincerely thank the Department of Electrical Engineering at Al-Mustansiriyah University’s College of Engineering for supporting this study.

  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: 2025-05-18
Accepted: 2025-08-18
Published Online: 2025-09-08

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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