Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Convolutional Neural Networks with Multiple Layers per Span for Nonlinearity Mitigation in Long-Haul WDM Transmission Systems. / Sidelnikov, Oleg; Redyuk, Alexey; Sygletos, Stylianos et al.
2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. Paper CI-P.6 (2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Convolutional Neural Networks with Multiple Layers per Span for Nonlinearity Mitigation in Long-Haul WDM Transmission Systems
AU - Sidelnikov, Oleg
AU - Redyuk, Alexey
AU - Sygletos, Stylianos
AU - Fedoruk, Mikhail
AU - Turitsyn, Sergei
N1 - Funding Information: The work was supported by the Russian Science Foundation (Grant No. 17-72-30006). Publisher Copyright: © 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - Overcoming fiber nonlinearity is one of the most challenging tasks in optical fiber links and it is a major limiting factor for extending their capacity. Digital backward propagation (DBP) method can be used to mitigate nonlinear transmission impairments [1] , but its complexity prevents any real-time implementation in these systems. On the other hand, it has been recently shown that deep neural networks can provide a good approximation of DBP at lower computational cost [2]. In this work, we continue the investigation of the proposed deep convolutional neural network (DCNN) [3] for long-haul WDM transmission systems. We study the effect of the number of neural network layers on the efficiency of nonlinear distortion compensation.
AB - Overcoming fiber nonlinearity is one of the most challenging tasks in optical fiber links and it is a major limiting factor for extending their capacity. Digital backward propagation (DBP) method can be used to mitigate nonlinear transmission impairments [1] , but its complexity prevents any real-time implementation in these systems. On the other hand, it has been recently shown that deep neural networks can provide a good approximation of DBP at lower computational cost [2]. In this work, we continue the investigation of the proposed deep convolutional neural network (DCNN) [3] for long-haul WDM transmission systems. We study the effect of the number of neural network layers on the efficiency of nonlinear distortion compensation.
UR - http://www.scopus.com/inward/record.url?scp=85117598539&partnerID=8YFLogxK
UR - https://www.elibrary.ru/item.asp?id=47516217
U2 - 10.1109/CLEO/Europe-EQEC52157.2021.9542317
DO - 10.1109/CLEO/Europe-EQEC52157.2021.9542317
M3 - Conference contribution
AN - SCOPUS:85117598539
T3 - 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021
BT - 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021
Y2 - 21 June 2021 through 25 June 2021
ER -
ID: 34536941