Optimizing FSO systems for 6G network using particle swarm optimization
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Oumaima Allaoua
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Hanane Djellab
Abstract
Free Space Optics (FSO) is an innovative optical communication technology that leverages light propagation in free space for direct line-of-sight interactions. This technology is characterized by several benefits, including secure transmission, high bandwidth, and rapid data transfer rates, making it a promising candidate for integration into emerging 6G networks. However, the efficiency of FSO systems is adversely affected by various weather conditions, such as rain, temperature fluctuations, snow, and fog, as well as atmospheric disturbances. This paper presents a Particle Swarm Optimization (PSO)-based approach to optimize the divergence angle of FSO systems. PSO is a type of artificial intelligence that excels at managing complex settings and adapting to changes in the environment, thus making FSO systems more robust against such challenges. By optimizing this critical parameter, the proposed method significantly improves the reliability and efficiency of data transmission in FSO systems, supporting the advanced performance demands of future 6G networks. This optimization ensures FSO systems can achieve faster and more reliable connectivity, essential for the next generation of wireless communication.
Acknowledgments
The authors are grateful to the anonymous referees for their valuable and helpful comments. The authors thank the staff of LAMIS laboratory for helpful comments and suggestions.
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Research ethics: This study adheres to ethical guidelines, ensuring informed consent from all participants and the responsible use of AI and machine learning tools. All research was conducted in compliance with relevant legal and ethical standards.
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Informed consent: Not applicable.
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Author contributions: Oumaima ALLAOUA: original draft preparation, Hanene DJALLAB: paper revision and validation, Fouzia MAAMRI: reviewing and editing, Yacine BELHOCINE: software, Riad SAIDI: methodology, Farouk BOUMEHREZ: conceptualization.
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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 state no conflict of interest.
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Research funding: This research has been carried out within the PRFU project (Grant: 2A01L08UN120120220001) of the Department of Electronics and Telecommunications, Larbi Tebessi University – Tebessa, Algeria.
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Data availability: Not applicable.
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