Capacity and security enhancement in TWDM-NG-PON2 networks based on hybrid algorithms for image transmission
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Ahmed Hussein Ali
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Hayder H. Al-khaylani
Abstract
As the number of users on optical access networks (OAN) has grown significantly, security concerns are growing. In order to solve this issue, a passive optical network (PON) second generation (TWDM-NG-PON2) design has employed bidirectional division multiplexing in both the time and wavelength domains. According to the International Telecommunication Union-Telecommunication (ITU-T) 989.2 series standards, the technique permits safe authentication mutual and key establishment between optical line termination (OLT) units and optical network units (ONUs) in a distribution optical network (ODN). The two algorithm ciphers have been utilize together with the Opti-System software. This work provides a new highly-speed grayscale image system transmission above bidirectional fiber link employing time and wavelength division multiplexing (TWDM) with two different algorithms for each wavelength. Two grayscale images are broadcast simultaneously at 20 Gbps each, for a total capacity of 40 Gbps. Based on secure TWDM-NG-PON2 technology, military and medical communications, digital watermarking, and E-commerce considered one of its applications. According to simulation studies, a bidirectional distance fiber with a ratio of splitting 1:64 can be successfully implemented at 55 km. High security, high capacity, and an advanced long-distance communication systems that delivers efficient usage source and strengthened authenticity network are all provided by the suggested algorithm in TWDM- NG-PON2.
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Research ethics: “The local Institutional Review Board deemed the study exempt from review” if the IRB specifically exempted the study from review.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: The authors have 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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Conflict of interest: The authors 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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