IEICE TRANSACTIONS on Communications
Demodulation Framework Based on Machine Learning for Unrepeated Transmission Systems
Summary :
We propose a demodulation framework to extend the maximum distance of unrepeated transmission systems, where the simplest back propagation (BP), polarization and phase recovery, data arrangement for machine learning (ML), and symbol decision based on ML are rationally combined. The deterministic waveform distortion caused by fiber nonlinearity and chromatic dispersion is partially eliminated by BP whose calculation cost is minimized by adopting the single-step Fourier method in a pre-processing step. The non-deterministic waveform distortion, i.e., polarization and phase fluctuations, can be eliminated in a precise manner. Finally, the optimized ML model conducts the symbol decision under the influence of residual deterministic waveform distortion that cannot be cancelled by the simplest BP. Extensive numerical simulations confirm that a DP-16QAM signal can be transmitted over 240km of a standard single-mode fiber without optical repeaters. The maximum transmission distance is extended by 25km.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E107-B No.1 pp.39-48
- Publication Date
- 2024/01/01
- Publicized
- 2023/09/14
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2023PNP0003
- Type of Manuscript
- Special Section PAPER (Special Section on Photonic Network Technology for Beyond 5G/6G Era)
- Category
Authors
Ryuta SHIRAKI
Kyoto University
Yojiro MORI
Nagoya University
Hiroshi HASEGAWA
Nagoya University
Keyword
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Ryuta SHIRAKI, Yojiro MORI, Hiroshi HASEGAWA, "Demodulation Framework Based on Machine Learning for Unrepeated Transmission Systems" in IEICE TRANSACTIONS on Communications,
vol. E107-B, no. 1, pp. 39-48, January 2024, doi: 10.1587/transcom.2023PNP0003.
Abstract: We propose a demodulation framework to extend the maximum distance of unrepeated transmission systems, where the simplest back propagation (BP), polarization and phase recovery, data arrangement for machine learning (ML), and symbol decision based on ML are rationally combined. The deterministic waveform distortion caused by fiber nonlinearity and chromatic dispersion is partially eliminated by BP whose calculation cost is minimized by adopting the single-step Fourier method in a pre-processing step. The non-deterministic waveform distortion, i.e., polarization and phase fluctuations, can be eliminated in a precise manner. Finally, the optimized ML model conducts the symbol decision under the influence of residual deterministic waveform distortion that cannot be cancelled by the simplest BP. Extensive numerical simulations confirm that a DP-16QAM signal can be transmitted over 240km of a standard single-mode fiber without optical repeaters. The maximum transmission distance is extended by 25km.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2023PNP0003/_p
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@ARTICLE{e107-b_1_39,
author={Ryuta SHIRAKI, Yojiro MORI, Hiroshi HASEGAWA, },
journal={IEICE TRANSACTIONS on Communications},
title={Demodulation Framework Based on Machine Learning for Unrepeated Transmission Systems},
year={2024},
volume={E107-B},
number={1},
pages={39-48},
abstract={We propose a demodulation framework to extend the maximum distance of unrepeated transmission systems, where the simplest back propagation (BP), polarization and phase recovery, data arrangement for machine learning (ML), and symbol decision based on ML are rationally combined. The deterministic waveform distortion caused by fiber nonlinearity and chromatic dispersion is partially eliminated by BP whose calculation cost is minimized by adopting the single-step Fourier method in a pre-processing step. The non-deterministic waveform distortion, i.e., polarization and phase fluctuations, can be eliminated in a precise manner. Finally, the optimized ML model conducts the symbol decision under the influence of residual deterministic waveform distortion that cannot be cancelled by the simplest BP. Extensive numerical simulations confirm that a DP-16QAM signal can be transmitted over 240km of a standard single-mode fiber without optical repeaters. The maximum transmission distance is extended by 25km.},
keywords={},
doi={10.1587/transcom.2023PNP0003},
ISSN={1745-1345},
month={January},}
Copy
TY - JOUR
TI - Demodulation Framework Based on Machine Learning for Unrepeated Transmission Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 39
EP - 48
AU - Ryuta SHIRAKI
AU - Yojiro MORI
AU - Hiroshi HASEGAWA
PY - 2024
DO - 10.1587/transcom.2023PNP0003
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E107-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2024
AB - We propose a demodulation framework to extend the maximum distance of unrepeated transmission systems, where the simplest back propagation (BP), polarization and phase recovery, data arrangement for machine learning (ML), and symbol decision based on ML are rationally combined. The deterministic waveform distortion caused by fiber nonlinearity and chromatic dispersion is partially eliminated by BP whose calculation cost is minimized by adopting the single-step Fourier method in a pre-processing step. The non-deterministic waveform distortion, i.e., polarization and phase fluctuations, can be eliminated in a precise manner. Finally, the optimized ML model conducts the symbol decision under the influence of residual deterministic waveform distortion that cannot be cancelled by the simplest BP. Extensive numerical simulations confirm that a DP-16QAM signal can be transmitted over 240km of a standard single-mode fiber without optical repeaters. The maximum transmission distance is extended by 25km.
ER -
English
