Digital backpropagation accounting for polarization-mode dispersion

Czegledi, Cristian Bogdan; Liga, Gabriele; Lavery, Domaniç; Karlsson, Magnus; Agrell, Erik; Savory, Seb J.; Bayvel, Polina

doi:10.1364/oe.25.001903

Cited by 32 publications

(26 citation statements)

References 26 publications

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“…Possible solutions could be use of pilot tones for timing synchronization or operation in weak coupling regime with sparsity managed MIMO equalizers [17,18]. Other transmission impairments such as the equalization enhanced phase noise [31] and fiber nonlinearities [32] could possibly impact the clock recovery performance and are planned to be analyzed in future works.…”

Section: Discussionmentioning

confidence: 99%

Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems

2018

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We present an analysis of clock recovery algorithms in both polarization division multiplexing systems and mode division multiplexing systems. The impact of inter-polarization time skew and polarization mode dispersion in single-mode fibers, as well as the combined impact of mode mixing and mode group delay spread in multi-mode fibers under different coupling regimes are investigated. Results show that although the clock tone vanishing has a known solution for single-mode systems, in multi-mode systems even for low group delay spread, strong coupling will cause clock tone extinction, making it harder to implement an effective clock recovery scheme.

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Section: Discussionmentioning

confidence: 99%

Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems

2018

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“…Different techniques have been proposed in previous works to embed the distributed compensation of PMD in the DBP algorithm, when the knowledge of the PMD evolution in the link is missing [36], [37], [59]. In this section, we describe how distributed PMD compensation can be combined with LDBP in a hardware-efficient manner.…”

Section: B Distributed Pmd Compensationmentioning

confidence: 99%

Revisiting Efficient Multi-Step Nonlinearity Compensation With Machine Learning: An Experimental Demonstration

Oliari

Goossens

Häger

et al. 2020

J. Lightwave Technol.

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Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch". One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance-complexity tradeoffs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud singlechannel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work.

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“…For the latter, it has been shown in [11,12] that a minimum 40 steps/span is required to eliminate non-linear distortions (also called as FS-DBP). It worth mentioning that DBP has been recently modified to account for PMD [43] and a stochastic-DBP was also designed to partly account for the ASE noise from optical amplification [44]. In [44], however, a maximum a posteriori principle was introduced with the help of Bayesian graphical models (a machine learning based approach) being combined with the deterministic DBP.…”

Section: Drawbacks and Deficiencies Of Benchmark Fiber Non-linearity mentioning

confidence: 99%

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

et al. 2018

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Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Future Internet 2019, 11, 2 2 of 20 between the spatial modes, compared to the SMFs. On the other hand, the drive towards higher-order modulation formats, such as 16-QAM, and spectral-efficient techniques, such as orthogonal frequency division multiplexing (OFDM), lead to greater transmission impairments, reducing the maximum distance over which increased capacity can be provided. More specific, denser constellation diagrams render higher-order modulation formats are more susceptible to circularly-symmetric Gaussian noise as generated by Erbium-doped fiber amplifiers (EDFAs) along the transmission link [6]. Even though the launch power per wavelength channel can be increased to improve the signal-to-noise ratio (SNR) at the receiver, transmission is limited by nonlinear distortions due to the Kerr effect, which have a more severe impact on higher-order modulation formats and spectral-efficient modulation schemes [5,7].Moreover, the transmission of more than two signal wavelengths (wavelength-division multiplexing, WDM) through an optical fibre generates four-wave mixing (FWM), a process caused by the power dependence of the refractive index of the optical fibre [3]. FWM is related to fibre nonlinearity and gives rise to new wavelengths which significantly degrade the signal quality especially at high optical powers and when signals are spectrally close to each other. FWM is one of the most dominant nonlinear effects in optical networks and a primary root of the capacity crunch [3]. Since nonlinear noise such as FWM is highly correlated to signals themselves, nonlinearity can be mitigated by performing special treatment of the signals or conducting post-transmission digital signal processing (DSP) on received signals [5,7].On the other hand, coherent optical OFDM (CO-OFDM) [8] has attracted a lot of interest in optical fiber communications due to its simplified DSP units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio (PAPR) imposes high vulnerability to fiber-induced nonlinearities [8]. Attempts to combat nonlinearities in CO-OFDM have been performed by deterministic nonlinearity compensators which take advantage of the fact that light scattering within a fibre is a deterministic process. Key te...

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Digital backpropagation accounting for polarization-mode dispersion

Cited by 32 publications

References 26 publications

Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems

Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems

Revisiting Efficient Multi-Step Nonlinearity Compensation With Machine Learning: An Experimental Demonstration

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

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