Design and performance evaluation of an optical communication system for 5G applications
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Omar Raad Kadhim
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Arwa A. Moosa
Abstract
With the escalating demand for high-capacity optical communication systems, particularly in the context of 5G networks, there is a critical need for transmission technologies that can support large data volumes over extended distances while minimizing signal attenuation. This study presents the design and performance evaluation of a dense wavelength division multiplexing (DWDM)-based optical communication system using OptiSystem software. The system was assessed under two scenarios to identify optimal parameters for high-performance data transmission. The primary objective was to improve transmission quality by minimizing dispersion and extending the reach of the optical link. Carrier-suppressed return-to-zero (CSRZ) modulation was identified as an effective format due to its improved spectral efficiency and compatibility with DWDM architectures. To counteract signal degradation caused by scattering, absorption, and losses, erbium-doped fiber amplifiers (EDFAs) with a gain of 15 dB were implemented. In the first scenario, signal power levels ranging from −15 dBm to 15 dBm and bit rates from 5 Gb/s to 40 Gb/s were simulated over a 100 km single-mode fiber link. System performance was analyzed using Q-factor values and eye diagram analysis across eight DWDM channels. The second scenario extended the transmission distance to 300 km, demonstrating a notable decline in Q-factor with increased fiber length, thereby emphasizing the importance of amplifier placement and modulation strategy in long-haul optical systems.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Mudhafar Hussein Ali designed the study; Omar Raad Kadhim collected the data; Samar K. Ibrahim performed analysis; all authors contributed to writing and approved the final manuscript. 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 state no conflict of interest.
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Research funding: None declared.
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Data availability: The raw data can be obtained on request from corresponding author.
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