Optical Phase Conjugation With Complex-Valued Deep Neural Network for WDM 64-QAM Coherent Optical Systems

Lei Wang; Mingyi Gao; Yongliang Zhang; Fengchu Cao; He Huang

doi:10.1109/JPHOT.2021.3111921

IEEE Photonics Journal (Jan 2021)

Optical Phase Conjugation With Complex-Valued Deep Neural Network for WDM 64-QAM Coherent Optical Systems

Lei Wang,
Mingyi Gao,
Yongliang Zhang,
Fengchu Cao,
He Huang

Affiliations

Lei Wang: ORCiD; Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network, Suzhou Key Laboratory of Advanced Optical Communication Network Technology, the School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
Mingyi Gao: ORCiD; Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network, Suzhou Key Laboratory of Advanced Optical Communication Network Technology, the School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
Yongliang Zhang: Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network, Suzhou Key Laboratory of Advanced Optical Communication Network Technology, the School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
Fengchu Cao: Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network, Suzhou Key Laboratory of Advanced Optical Communication Network Technology, the School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
He Huang: ORCiD; Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network, Suzhou Key Laboratory of Advanced Optical Communication Network Technology, the School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China

DOI: https://doi.org/10.1109/JPHOT.2021.3111921
Journal volume & issue: Vol. 13, no. 5
pp. 1 – 8

Abstract

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We experimentally demonstrated a photoelectric nonlinear compensation scheme of optical phase conjugation (OPC) with complex-valued deep neural network (CVDNN) to mitigate fiber nonlinearity in wavelength division multiplexing (WDM) 64-QAM coherent optical transmission system. The factors to affect the performance of OPC and CVDNN are comprehensively considered. OPC in WDM system is experimentally optimized to alleviate the deployment requirements of strict symmetrical distributed power and chromatic dispersion. The performance penalty caused by the simplification of the OPC is further compensated by the CVDNN. The selections of the input neurons’ number and the optimization algorithm are also considered to design a simple two-hidden-layer-structure CVDNN. The proposed method is experimentally verified and evaluated in a 12.5-GBd 4-channel WDM 64-QAM 160-km standard single-mode fiber (SSMF) transmission system with channel spacing of 50-GHz. The experimental results show that the proposed nonlinear equalizer based on the OPC with CDVNN has a strong robustness to the input signal power and wavelength, which can not only improve the Q factor of the signal by 1.5-dB, but also greatly increase the total launched signal power.

Published in IEEE Photonics Journal

ISSN: 1943-0655 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Engineering (General). Civil engineering (General): Applied optics. Photonics; Science: Physics: Optics. Light
Website: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4563994

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