Performance investigation of FSO-WDM system employing ML under dynamic environmental conditions
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Kritica Sharma
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Hardeep Singh
Abstract
The primary objective of the present work is to enhance the performance of free space optical – wavelength division multiplexing (FSO-WDM) system under varying atmospheric conditions. The FSO-WDM network leverages 12 continuous wave (CW) laser sources with a channel spacing of 0.5 nm, transmitting data at 5 Gbps per channel using nonreturn-to-zero (NRZ) modulation. The quality factor (Q-factor) of received signal has been ascertained as a function of transmission wavelength at each node considering clear air, fog, and rainy weather conditions. The proposed system demonstrates superior performance with deployment of repeaters at 1 km intervals of the FSO channel resulting in achievement of higher Q factor (35) at a wavelength of 1,550 nm in case of rainy season. A machine learning (ML) model supported by genetic algorithm (GA) has also been implemented to dynamically optimize the performance of the system in terms of Q factor and increase the power efficiency by 5 %.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Competing interests: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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