scholarly journals End-to-End Optimization of Coherent Optical Communications Over the Split-Step Fourier Method Guided by the Nonlinear Fourier Transform Theory

2021 ◽  
Vol 39 (2) ◽  
pp. 418-428
Author(s):  
Simone Gaiarin ◽  
Francesco Da Ros ◽  
Rasmus T. Jones ◽  
Darko Zibar
2020 ◽  
Vol 45 (13) ◽  
pp. 3462 ◽  
Author(s):  
Oleksandr Kotlyar ◽  
Maryna Pankratova ◽  
Morteza Kamalian-Kopae ◽  
Anastasiia Vasylchenkova ◽  
Jaroslaw E. Prilepsky ◽  
...  

2017 ◽  
Vol 35 (23) ◽  
pp. 5056-5068 ◽  
Author(s):  
Ivan T. Lima ◽  
Thiago D. S. DeMenezes ◽  
Vladimir S. Grigoryan ◽  
Maurice O'Sullivan ◽  
Curtis R. Menyuk

2017 ◽  
Vol 35 (9) ◽  
pp. 1542-1550 ◽  
Author(s):  
Tao Gui ◽  
Terence H. Chan ◽  
Chao Lu ◽  
Alan Pak Tao Lau ◽  
Ping-Kong Alexander Wai

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Srikanth Sugavanam ◽  
Morteza Kamalian Kopae ◽  
Junsong Peng ◽  
Jaroslaw E. Prilepsky ◽  
Sergei K. Turitsyn

AbstractModern high-power lasers exhibit a rich diversity of nonlinear dynamics, often featuring nontrivial co-existence of linear dispersive waves and coherent structures. While the classical Fourier method adequately describes extended dispersive waves, the analysis of time-localised and/or non-stationary signals call for more nuanced approaches. Yet, mathematical methods that can be used for simultaneous characterisation of localized and extended fields are not yet well developed. Here, we demonstrate how the Nonlinear Fourier transform (NFT) based on the Zakharov-Shabat spectral problem can be applied as a signal processing tool for representation and analysis of coherent structures embedded into dispersive radiation. We use full-field, real-time experimental measurements of mode-locked pulses to compute the nonlinear pulse spectra. For the classification of lasing regimes, we present the concept of eigenvalue probability distributions. We present two field normalisation approaches, and show the NFT can yield an effective model of the laser radiation under appropriate signal normalisation conditions.


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