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Computing continuous nonlinear fourier spectrum of optical signal with artificial neural networks. / Sedov, Egor; Prilepsky, Jaroslaw; Chekhovskoy, Igor et al.

European Quantum Electronics Conference, EQEC 2021. The Optical Society, 2021. ej_1_4 (Optics InfoBase Conference Papers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Sedov, E, Prilepsky, J, Chekhovskoy, I & Turitsyn, S 2021, Computing continuous nonlinear fourier spectrum of optical signal with artificial neural networks. in European Quantum Electronics Conference, EQEC 2021., ej_1_4, Optics InfoBase Conference Papers, The Optical Society, 2021 European Quantum Electronics Conference, EQEC 2021 - Part of 2021 Conference on Lasers and Electro-Optics Europe, CLEO 2021, Virtual, Online, Germany, 21.06.2021.

APA

Sedov, E., Prilepsky, J., Chekhovskoy, I., & Turitsyn, S. (2021). Computing continuous nonlinear fourier spectrum of optical signal with artificial neural networks. In European Quantum Electronics Conference, EQEC 2021 [ej_1_4] (Optics InfoBase Conference Papers). The Optical Society.

Vancouver

Sedov E, Prilepsky J, Chekhovskoy I, Turitsyn S. Computing continuous nonlinear fourier spectrum of optical signal with artificial neural networks. In European Quantum Electronics Conference, EQEC 2021. The Optical Society. 2021. ej_1_4. (Optics InfoBase Conference Papers).

Author

Sedov, Egor ; Prilepsky, Jaroslaw ; Chekhovskoy, Igor et al. / Computing continuous nonlinear fourier spectrum of optical signal with artificial neural networks. European Quantum Electronics Conference, EQEC 2021. The Optical Society, 2021. (Optics InfoBase Conference Papers).

BibTeX

@inproceedings{8cd26e08fff84b11896f1eadd73bb922,
title = "Computing continuous nonlinear fourier spectrum of optical signal with artificial neural networks",
abstract = "We propose the artificial neural network architecture that can efficiently perform the nonlinear Fourier optical signal processing. The performance of the new method is analysed considering the error between the precomputed and predicted nonlinear spectra.",
author = "Egor Sedov and Jaroslaw Prilepsky and Igor Chekhovskoy and Sergei Turitsyn",
note = "Funding Information: This work was supported by the RSF grant 17-72-30006 (ES, ST), by the grant of the President of the RF MK-677.2020.9 (IC), by the EPSRC grant TRANSNET (ES, ST), Leverhulme Trust project RPG-2018-063 (JP, SK). References [1] V. Zakharov and A. Shabat, “Exact theory of two-dimensional self-focusing and one-dimensional self-modulation of waves in nonlinear media,” Sov. Phys. JETP 34, 62–69 (1972). [2] S. K. Turitsyn, et al. “Nonlinear Fourier transform for optical data processing and transmission: advances and perspectives,” Optica 4, 307–322 (2017). [3] S. Turitsyn, E. Sedov, A. Redyuk, and M. Fedoruk, “Nonlinear spectrum of conventional OFDM and WDM return-to-zero signals in nonlinear channel,” J. Light. Technol. 38, 352–358 (2019).; 2021 European Quantum Electronics Conference, EQEC 2021 - Part of 2021 Conference on Lasers and Electro-Optics Europe, CLEO 2021 ; Conference date: 21-06-2021 Through 25-06-2021",
year = "2021",
language = "English",
series = "Optics InfoBase Conference Papers",
publisher = "The Optical Society",
booktitle = "European Quantum Electronics Conference, EQEC 2021",
address = "United States",

}

RIS

TY - GEN

T1 - Computing continuous nonlinear fourier spectrum of optical signal with artificial neural networks

AU - Sedov, Egor

AU - Prilepsky, Jaroslaw

AU - Chekhovskoy, Igor

AU - Turitsyn, Sergei

N1 - Funding Information: This work was supported by the RSF grant 17-72-30006 (ES, ST), by the grant of the President of the RF MK-677.2020.9 (IC), by the EPSRC grant TRANSNET (ES, ST), Leverhulme Trust project RPG-2018-063 (JP, SK). References [1] V. Zakharov and A. Shabat, “Exact theory of two-dimensional self-focusing and one-dimensional self-modulation of waves in nonlinear media,” Sov. Phys. JETP 34, 62–69 (1972). [2] S. K. Turitsyn, et al. “Nonlinear Fourier transform for optical data processing and transmission: advances and perspectives,” Optica 4, 307–322 (2017). [3] S. Turitsyn, E. Sedov, A. Redyuk, and M. Fedoruk, “Nonlinear spectrum of conventional OFDM and WDM return-to-zero signals in nonlinear channel,” J. Light. Technol. 38, 352–358 (2019).

PY - 2021

Y1 - 2021

N2 - We propose the artificial neural network architecture that can efficiently perform the nonlinear Fourier optical signal processing. The performance of the new method is analysed considering the error between the precomputed and predicted nonlinear spectra.

AB - We propose the artificial neural network architecture that can efficiently perform the nonlinear Fourier optical signal processing. The performance of the new method is analysed considering the error between the precomputed and predicted nonlinear spectra.

UR - http://www.scopus.com/inward/record.url?scp=85119952391&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85119952391

T3 - Optics InfoBase Conference Papers

BT - European Quantum Electronics Conference, EQEC 2021

PB - The Optical Society

T2 - 2021 European Quantum Electronics Conference, EQEC 2021 - Part of 2021 Conference on Lasers and Electro-Optics Europe, CLEO 2021

Y2 - 21 June 2021 through 25 June 2021

ER -

ID: 34838766