Performance evaluation of dispersion compensation techniques in optical fiber networks
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Shivani Goyal
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Hunny Pahuja
Abstract
In optical fiber communication, signals are transmitted as light pulses over long distances through optical fibers. During transmission, these pulses experience losses primarily due to attenuation and dispersion, which degrade the system’s overall performance and reduce available bandwidth. As modern applications demand higher data rates and broader bandwidth, managing dispersion becomes increasingly important. Dispersion is a key factor that limits transmission efficiency by causing pulse broadening, which in turn shortens the transmission distance and lowers the achievable bit rate. To enhance system performance and support higher bit rates, various dispersion compensation techniques are employed. In this study, we use OptiSystem version 7.0 to model and evaluate the effectiveness of different compensation methods – pre-compensation, post-compensation, and symmetrical compensation. Dispersion compensating fiber (DCF) is used across varying transmission lengths to analyse its impact. Performance metrics such as quality factor (QF), bit error rate (BER), and eye diagrams are assessed at the receiver to determine the effectiveness of each compensation technique.
Acknowledgments
The authors would like to thank their respective institutions for providing the necessary support and infrastructure for this research.
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Research ethics: This study did not involve any experiments with human participants or animals and thus did not require ethical approval.
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Informed consent: Not applicable. No human participants were involved in this research.
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Author contributions: [Shivani Goyal, Hunny Pahuja] conceived the idea and conducted the experiments. [Manoj Sindhwani, Shippu Sachdeva] analyzed the data and drafted the manuscript. All authors reviewed the final version.
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Use of Large Language Models, AI and Machine Learning Tools: ChatGPT was used solely for refining the language and grammar of the manuscript. No AI tools were used for data analysis or generating scientific content.
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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: Not applicable.
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