Performance enhancement in coherent optical systems using unequal channel spacing
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Sama Salam Saleh
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Qussay Salim Al-Jubouri
Abstract
An essential part of communication networks are data representations, this has an impact on transmission distance, maximum bit rate, system capacity, and various linear and nonlinear. In order to eliminate effects, the proposed approach provides long-distance optical transmission at high rates with using 12.5 GHz unequal channel spacing. Polarization Division Multiplexing (PDM) is also demonstrated as a technique to enhance Spectral Efficiency. Opti System (2017) Version 15 is used to simulate this work. A 100 Gbps data rate over a 1,000 km transmission distance is being investigated, and two data formats, Quadrature Amplitude Modulation (16-QAM) and Quadrature Phase Shift Keying (QPSK), are being compared; these representations are realized using (4 and 8) Dense Wavelength Division Multiplexing (DWDM) channels. The QPSK format achieves the best −3.67 Bit Error Rate (BER) at the receiver over that distance while using one Erbium-Doped Fiber Amplifier (EDFA) at −1 dBm optimal power and maximum OSNR value at utilizing 16-QAM is 24.42 dB. In addition, we investigate how an advanced fiber system with a high effective area and ultra-low loss impacts the performance of 16-QAM, considering into consideration both linear and nonlinear effects, the result achieved 25.81 dB at −2 dBm optimal launched power. Approximately 20 % of the combined Forward Error Correction (FEC) threshold is equal to the 0.033 BER baseline level.
Acknowledgments
The authors gratefully acknowledge the support of the department of communication engineering, University of Technology for their encouragement.
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Research ethics: In this paper will provide a concise overview of the high and low-order coherent modulation types, highlighting using unequal channel spacing with EDFA amplifiers for long transmission distance.
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Informed consent: Not applicable.
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Author contributions: S.S.S, wrote the initial manuscript draft, conceptualization and methodology in consultation with Q.A.J., supervision and validation.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: Authors declare that they have no conflict of interest.
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Research funding: None declared.
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Data availability: This includes the paper itself.
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