M.P. Fedoruk
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
M.P. Fedoruk has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Oleg S. Sidelnikov
Department of Physics, Novosibirsk State University, 1 Pirogova St., 630090 Novosibirsk, Russia, Russia
2 shared publications - Alexey Redyuk
Novosibirsk State University
1 shared publication - Evgenii V. Podivilov
Institute of Automation and Electrometry, Russian Academy of Sciences , 630090 Novosibirsk
1 shared publication - Evgeny Averyanov
Mathematics and Mechanics, Novosibirsk State University, 64941 Novosibirsk, Novosibirskaya oblast Russian Federation 630090
1 shared publication - Sergei Konstantinovich Turitsyn
Aston Institute of Photonic Technologies (AIPT), Aston University, Birmingham, UK
1 shared publication - Sergey A. Babin
Institute of Automation and Electrometry, 1 Ac. Koptyug Ave., Novosibirsk 630090, Russia
1 shared publication
Latest Publications
- 1 December 2020
- Published by IOP Publishing in Quantum Electronics
- Vol. 50 (12) , 1101-1104
Numerical simulation of the beam self-cleaning process in a multimode graded-index fibre during propagation of a pump wave and a Stokes component
A model of coupled modes is proposed, which makes it possible to describe the propagation of a pump wave and a Stokes component in a multimode graded-index fibre. A decrease in the depression in the centre of the profile of the pump wave intensity distribution as a result of the influence of Kerr effects and random linear coupling of modes is demonstrated.
- 1 December 2020
- Published by IOP Publishing in Quantum Electronics
- Vol. 50 (12) , 1101-1104
Numerical simulation of the beam self-cleaning process in a multimode graded-index fibre during propagation of a pump wave and a Stokes component
A model of coupled modes is proposed, which makes it possible to describe the propagation of a pump wave and a Stokes component in a multimode graded-index fibre. A decrease in the depression in the centre of the profile of the pump wave intensity distribution as a result of the influence of Kerr effects and random linear coupling of modes is demonstrated.
- 5 February 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Journal of Lightwave Technology
- Vol. 38 (6) , 1250-1257
Compensation of Nonlinear Impairments Using Inverse Perturbation Theory With Reduced Complexity
We propose a modification of the conventional perturbation-based approach of fiber nonlinearity compensation that enables straight-forward implementation at the receiver and meets feasible complexity requirements. We have developed a model based on perturbation analysis of an inverse Manakov problem, where we use the received signal as the initial condition and solve Manakov equations in the reversed direction, effectively implementing a perturbative digital backward propagation enhanced by machine learning techniques. To determine model coefficients we employ machine learning methods using a training set of transmitted symbols. The proposed approach allowed us to achieve 0.5 dB and 0.2 dB $Q^2$ -factor improvement for 2000 km transmission of 11x256 Gbit/s DP-16QAM signal compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We quantify the trade-off between performance and complexity.- 5 February 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Journal of Lightwave Technology
- Vol. 38 (6) , 1250-1257
Compensation of Nonlinear Impairments Using Inverse Perturbation Theory With Reduced Complexity
We propose a modification of the conventional perturbation-based approach of fiber nonlinearity compensation that enables straight-forward implementation at the receiver and meets feasible complexity requirements. We have developed a model based on perturbation analysis of an inverse Manakov problem, where we use the received signal as the initial condition and solve Manakov equations in the reversed direction, effectively implementing a perturbative digital backward propagation enhanced by machine learning techniques. To determine model coefficients we employ machine learning methods using a training set of transmitted symbols. The proposed approach allowed us to achieve 0.5 dB and 0.2 dB $Q^2$ -factor improvement for 2000 km transmission of 11x256 Gbit/s DP-16QAM signal compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We quantify the trade-off between performance and complexity.