Submarine Transmission Systems Using Digital Nonlinear Compensation and Adaptive Rate Forward Error Correction

Ghazisaeidi, Amirhossein; Ruiz, Ivan Fernández de Jáuregui; Schmalen, Laurent; Tran, P.; Simonneau, C.; Awwad, Élie; Ušćumlić, Bogdan; Brindel, P.; Charlet, G.

doi:10.1109/jlt.2016.2518929

Cited by 54 publications

(13 citation statements)

References 29 publications

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“…The first-order linear perturbation of NLSE has led to the single-step perturbation-based pre/post-distortion (PPD) algorithm for Gaussian 10 and root-raised cosine (RRC) pulses 11–13 . PPD was demonstrated over transoceanic distances to achieve only slightly less NLC gain than filtered DBP at 0.5 SpS 12 . Furthermore, after the initial success of filtered DBP and PPD in reducing the complexity, further reduction of complexity proved difficult to achieve.…”

Section: Introductionmentioning

confidence: 94%

Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

Zhang¹,

Yaman²,

Nakamura

et al. 2019

Nat Commun

121

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Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger equation through digital back propagation, or a single step approach based on perturbation methods have been demonstrated, however, their implementation demands excessive signal processing resources, and accurate knowledge of the transmission system. A completely different approach uses machine learning algorithms to learn from the received data itself to figure out the nonlinear impairment. In this work, a single-step, system agnostic nonlinearity compensation algorithm based on a neural network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6 dB Q improvement after 2800 km standard single-mode fiber transmission using 32 Gbaud signal. Without prior knowledge of the transmission system, the neural network tensor weights are constructed from training data thanks to the intra-channel cross-phase modulation and intra-channel four-wave mixing triplets used as input features.

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Section: Introductionmentioning

confidence: 94%

Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

Zhang¹,

Yaman²,

Nakamura

et al. 2019

Nat Commun

121

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“…The most classical DBP implementation is based on the split-step Fourier method (SSFM) [1] , with an accuracy and complexity that increase with the number of steps. The gains achievable with DBP are well studied, both numerically and experimentally [22], [34], [35] . Unfortunately, reasonable gains require many steps (one or more per span), so that the search for less complex algorithms is still in progress.…”

Section: Digital Backpropagationmentioning

confidence: 99%

Tailored Shaping, Improved Detection, Simpler Backpropagation: the Road to Nonlinearity Mitigation

Secondini¹,

Civelli²,

Forestieri³

2021

Preprint

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“…In practice, there are usually two ways to implement the RPBP-NLC, which are the pre-distortion nonlinear compensation (RPBP-pre-NLC) and the post nonlinear compensation (RPBP-post-NLC). These methods have been extensively studied in the multi-span long-haul fiber communication systems and both methods are believed to demonstrate effective compensation and lower computational complexity compared with the SSFM-BP-NLC method [22]- [24], [26]. However, there is a gap in the current studies regarding the performance of the RPBP-NLC methods in the unrepeatered system with Raman amplification.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Analyses of Different Nonlinear Compensation Methods Based on Perturbation Theories in the Unrepeatered System With Raman Amplification

You

Mei

et al. 2020

IEEE Photonics J.

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In this paper, the pre-and post-nonlinear compensation (NLC) methods based on regular perturbation (RP) theory are contrastively investigated with the original NLC as a bridge for analyses. Firstly, the numerical error functions of pre-NLC and original NLC are derived, revealing that the numerical error of pre-NLC is more severe due to the error accumulation. Secondly, we deduce the relevance of post-NLC and original NLC, which uncovers the essential difference is that the input condition of post-NLC possesses certain additional information, making the signal's intensity distribution after post-NLC more beneficial for the hard-decision. Meanwhile, the pre-, post-and original NLC methods based on enhanced regular perturbation (ERP) theory have also been discussed. Finally, the simulation is carried out with signal modulation of 16/32/64 QAM and baud-rate of 32 GBaud in an unrepeartered system with Raman amplification. The results agree with the analyses, the post-NLC is the best while the original NLC surpasses the pre-NLC. Additionally, we demonstrate an experiment with signal modulation of 16 QAM and baud-rate of 10 GBaud. The improvements of the Q 2-factor of the RP-based and ERP-based post-NLC are about 0.6 dB and 1 dB compared with the electronic dispersion compensation (EDC).

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Submarine Transmission Systems Using Digital Nonlinear Compensation and Adaptive Rate Forward Error Correction

Cited by 54 publications

References 29 publications

Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

Tailored Shaping, Improved Detection, Simpler Backpropagation: the Road to Nonlinearity Mitigation

Theoretical Analyses of Different Nonlinear Compensation Methods Based on Perturbation Theories in the Unrepeatered System With Raman Amplification

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