Modified modelling of 16-channels WDM-RoF system in the presence of OPC and FBG for 5g applications
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Pravesh Kumari
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Vinod Kumar
Abstract
This paper introduces an innovative approach that employs wavelength division multiplexing-radio-over-fiber (WDM-RoF) technology to facilitate high-speed data transmission across an extended distance of 340 km. With the rapid growth of next-generation wireless networks, there is an increasing demand for cost-effective and energy-efficient optical communication systems that can deliver higher data rates over long-haul links without compromising signal integrity or overall network reliability. To address this requirement, the proposed model demonstrates a 16-channel WDM-RoF system that integrates optical phase conjugation (OPC) and fiber Bragg grating (FBG) for enhanced performance. The use of OPC effectively mitigates nonlinear distortions accumulated during fiber transmission, while the inclusion of FBG provides precise wavelength filtering and stabilization, thereby improving spectral efficiency, minimizing crosstalk, and reducing interference between densely packed channels. The system successfully achieves a transmission rate of 12 Gbps per channel at a carrier frequency of 250 GHz, maintaining a typical bit error rate (BER) of 10−9 across the 340 km span. Such performance validates the robustness and scalability of the proposed configuration for long-distance, high-capacity optical communication. The main objective of this work is to enable faster and more reliable data transmission using a single fiber optic cable, making it a promising and practical solution for emerging 5G as well as future 6G wireless communication infrastructures.
Acknowledgments
The authors would like to thank Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India, for providing the necessary facilities and support during this research.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have read and approved the final manuscript and accept full responsibility for its content and submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare no conflict of interest.
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Research funding: None declared.
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Data availability: The data supporting the findings of this study are available from the corresponding author upon reasonable request.
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