We propose a new machine-learning approach for fiber-optic communication systems
whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our …

A deep learning (DL) based digital backpropagation (DBP) method with a 1 dB SNR gain
over a conventional 1 step per span DBP is demonstrated in a 32 GBd 16QAM transmission …

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 …

Ultrashort pulses have a crucial role in the evolution of different areas of science such as
ultra fast imaging, femtochemistry and high harmonic spectroscopy and therefore …

This paper proposes a parametric network for the joint compensation of multiple linear
impairments in coherent optical communication systems. The considered linear impairments …

A method for reducing the training time of a deep learning based digital backpropagation
(DL-DBP) is presented. The method is based on dividing a link into smaller sections. A …

Neural networks (NNs) are attractive for nonlinear impairment compensation applications in
communication systems, such as optical fiber nonlinearity, nonlinearity of driving amplifiers …

In this paper, a method to suppress the impact of the nonlinear effects on an all optical
orthogonal frequency division multiplexing (AO-OFDM) system is proposed. By inserting a …

A novel learned modified perturbation backpropagation (L-MPBP) algorithm for high-symbol-
rate (HSR) coherent optical transmission system is proposed in this paper. As an extension …

Global demand for internet traffic is growing at a rapid rate, driven by the adoption of new
technologies and increased demand from consumers. This continued growth is exerting …