Compensation of Dispersion and Nonlinear Impairments Using Digital Backpropagation

By Ezra Ip, Joseph M. Kahn. Journal of Lightwave Technology, 2008

1,116 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/09b0ea4c472bd6d9220a2c232ec7d12d22633c47

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In Atomic and Molecular Physics, and Optics

In Electrical and Electronic Engineering

Citations in the corpus

Intrachannel Nonlinearity Compensation by Inverse Volterra Series Transfer Function (2012, background): “But for BP, there is obvious performance degradation with baud rate [2].”
Nonlinear Compensation Using Backpropagation for Polarization-Multiplexed Transmission (2010, methodology): “Since amplifier noise causes uncertainty in signal amplitude, it has been shown that BP should only derotate a fraction of the nonlinearity at each step, with being the nonlinear rotation parameter to be optimized that depends on the dispersion map and launch power [7].”
Inter-Channel Nonlinear Interference Noise in WDM Systems: Modeling and Mitigation (2015, background): “In principle, while intra-channel distortions can be eliminated by means of digital back propagation at the receiver [5], or pre-distortion at the transmitter [6], similar elimination of inter-channel distortions is considered impractical.”
Nonlinear Fourier Transform for Optical Data Processing and Transmission: Advances and Perspectives (2017, background): “As a result, so far the NFTbased DBP approach was elaborated only for the soliton-free case [50, 73].”
Multiplier-Free Intrachannel Nonlinearity Compensating Algorithm Operating at Symbol Rate (2011, background): “The most widely studied algorithm is the backpropagation at the receiver [8]–[11].”
Fiber Impairment Compensation Using Coherent Detection and Digital Signal Processing (2010, methodology): ““Intrachannel four-wave mixing” (IFWM) causes NLD to fail [55] (Fig.”
Modeling of Nonlinear Propagation in Space-Division Multiplexed Fiber-Optic Transmission (2016, neutral): “Subsequently, in Section VII we show that the presence of MD significantly reduces the magnitude of the intra-channel NLIN, although, at the same time, it eliminates the benefit that could be extracted from the deployment of digital backpropagation [21], [22].”
The GN-Model of Fiber Non-Linear Propagation and its Applications (2014, methodology): “The outcome of our analytical investigation is in good general agreement with the simulative and experimental results reported in [74]–[78].”
End-to-End Deep Learning of Optical Fiber Communications (2018, background): “for the realization of low-complexity nonlinearity compensation by digital backpropagation (DBP) [15].”
An Overview on Application of Machine Learning Techniques in Optical Networks (2018, methodology): “In [81], a neural network approximates very efficiently the function of digital backpropagation (DBP), which is a well-known technique to solve the non-linear Schroedinger equation using split-step Fourier method (SSFM) [110].”