Evolution from 8QAM live traffic to PS 64-QAM with Neural-Network Based Nonlinearity Compensation on 11000 km Open Subsea Cable

Kamalov, Valey; Jovanovski, Ljupcho; Vusirikala, Vijay; Zhang, Shaoliang; Yaman, Fatih; Nakamura, Kohei; Inoue, Takanori; Mateo, Eduardo; Inada, Yoshihisa

doi:10.1364/ofc.2018.th4d.5

Cited by 45 publications

(26 citation statements)

References 9 publications

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“…Various ML techniques, such as expectation maximization (EM) 11 , support vector machine (SVM) 12,13 , and message-passing algorithms 5 , were studied, but they show meaningful gains only for dispersion-managed links or OFDM signals, both of which are not default choices of technology in current long-haul digital coherent systems. For single-carrier systems, Kamalov et al 14 conducted a field-trial demonstration using neural networks with information symbol triplets as inputs, but the performance is inferior to standard DBP. On the other hand, Häger and Pfister [15][16][17] considered the linear and nonlinear steps of DBP as a deep neural network (DNN) where preliminary simulation studies for single-channel single-polarization systems are presented.…”

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confidence: 99%

Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

et al. 2020

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In long-haul optical communication systems, compensating nonlinear effects through digital signal processing (DSP) is difficult due to intractable interactions between Kerr nonlinearity, chromatic dispersion (CD) and amplified spontaneous emission (ASE) noise from inline amplifiers. Optimizing the standard digital back propagation (DBP) as a deep neural network (DNN) with interleaving linear and nonlinear operations for fiber nonlinearity compensation was shown to improve transmission performance in idealized simulation environments. Here, we extend such concepts to practical single-channel and polarization division multiplexed wavelength division multiplexed experiments. We show improved performance compared to state-of-the-art DSP algorithms and additionally, the optimized DNNbased DBP parameters exhibit a mathematical structure which guides us to further analyze the noise statistics of fiber nonlinearity compensation. This machine learning-inspired analysis reveals that ASE noise and incomplete CD compensation of the Kerr nonlinear term produce extra distortions that accumulates along the DBP stages. Therefore, the best DSP should balance between suppressing these distortions and inverting the fiber propagation effects, and such trade-off shifts across different DBP stages in a quantifiable manner. Instead of the common 'black-box' approach to intractable problems, our work shows how machine learning can be a complementary tool to human analytical thinking and help advance theoretical understandings in disciplines such as optics.

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confidence: 99%

Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

et al. 2020

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“…The presented BtB setup focuses on component nonlinearities, while the compensation of fiber nonlinearities has been shown before [7]. For this paper, DNN structures have been implemented from scratch with Matlab, not using any machine learning (ML) toolbox.…”

Section: Principles Of Nonlinear Equalizermentioning

confidence: 99%

Deep Neural Network Equalization for Optical Short Reach Communication

et al. 2019

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Nonlinear distortion has always been a challenge for optical communication due to the nonlinear transfer characteristics of the fiber itself. The next frontier for optical communication is a second type of nonlinearities, which results from optical and electrical components. They become the dominant nonlinearity for shorter reaches. The highest data rates cannot be achieved without effective compensation. A classical countermeasure is receiver-side equalization of nonlinear impairments and memory effects using Volterra series. However, such Volterra equalizers are architecturally complex and their parametrization can be numerical unstable. This contribution proposes an alternative nonlinear equalizer architecture based on machine learning. Its performance is evaluated experimentally on coherent 88 Gbaud dual polarization 16QAM 600 Gb/s back-to-back measurements. The proposed equalizers outperform Volterra and memory polynomial Volterra equalizers up to 6th orders at a target bit-error rate (BER) of 10 − 2 by 0.5 dB and 0.8 dB in optical signal-to-noise ratio (OSNR), respectively.

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“…Even though a functional NN could be built to serve the classification purpose from the former approach, the simulation and experimental results of the latter has shown significant improvement. Kamalov et al proposed an artificial-intelligence NLC (AI-NLC) algorithm considering "triplets" generated based on intra-channel cross-phase (IXPM) modulation and intra-channel four-wave mixing (IFWM) in time-domain perturbation pre/post-distortion (PPD) algorithm [9,10]. Based on a similar concept of interpreting Tx symbols into input features fed into neural networks during training period, a field and lab experiment of nonlinearity compensation was conducted, and the obtained neural network model demonstrates the capability of being applied without the necessities of acquiring information of link parameters hence transparent to transmission systems.…”

Section: A Neural Networkmentioning

confidence: 99%

Machine Learning for Fiber Nonlinearity Mitigation in Long-Haul Coherent Optical Transmission Systems : Invited Paper

Liu

Yang

2019

2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT)

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Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.

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Evolution from 8QAM live traffic to PS 64-QAM with Neural-Network Based Nonlinearity Compensation on 11000 km Open Subsea Cable

Cited by 45 publications

References 9 publications

Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

Deep Neural Network Equalization for Optical Short Reach Communication

Machine Learning for Fiber Nonlinearity Mitigation in Long-Haul Coherent Optical Transmission Systems : Invited Paper

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