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  1. Nonlinear Spectrum of Conventional OFDM and WDM Return-to-Zero Signals in Nonlinear Channel

    Турицын, С. К., Седов, Е. В., Редюк, А. А. & Федорук, М. П., 15 Jan 2020, In: Journal of Lightwave Technology. 38, 2, p. 352-358 7 p., 8915744.

    Research output: Contribution to journal › Article › peer-review

  2. Nonlinearity compensation techniques using machine learning

    Sygletos, S., Redyuk, A. & Sidelnikov, O., 1 Jan 2019, Signal Processing in Photonic Communications, SPPCom 2019. OSA Publishing, (Optics InfoBase Conference Papers; vol. Part F137-SPPCom 2019).

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

  3. Nonlinear Equalization in Long Haul Transmission Systems Using Dynamic Multi-Layer Perceptron Networks

    Sidelnikov, O., Redyuk, A. & Sygletos, S., 14 Nov 2018, 2018 European Conference on Optical Communication, ECOC 2018. Institute of Electrical and Electronics Engineers Inc., Vol. 2018-September. 8535144. (European Conference on Optical Communication, ECOC; vol. 2018-September).

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

  4. Nonlinear effects in optical signal transmission using a multimode fibre with weak coupling

    Sidelnikov, O. S. & Redyuk, A. A., 2017, In: Quantum Electronics. 47, 4, p. 330-334 5 p.

    Research output: Contribution to journal › Article › peer-review

  5. ML-Assisted Particle Swarm Optimization of a Perturbation-Based Model for Nonlinearity Compensation in Optical Transmission Systems

    Redyuk, A., Shevelev, E., Danilko, V. & Fedoruk, M., 1 Jan 2024, In: Journal of Lightwave Technology. p. 1-8

    Research output: Contribution to journal › Article › peer-review

  6. Methods for compensation of nonlinear effects in multichannel data transfer systems based on dynamic neural networks

    Sidelnikov, O. S., Redyuk, A. A., Sygletos, S. & Fedoruk, M. P., 11 Oct 2019, In: Quantum Electronics. 49, 12, p. 1154-1157 4 p.

    Research output: Contribution to journal › Article › peer-review

  7. Machine Learning Methods for Compensating Signal Distortions in Fiber-Optic Communication Lines

    Sidelnikov, O. S., Redyuk, A. A. & Fedoruk, M. P., Feb 2024, In: Optoelectronics, Instrumentation and Data Processing. 60, 1, p. 1-10 10 p.

    Research output: Contribution to journal › Article › peer-review

  8. Invited Article: Visualisation of extreme value events in optical communications

    Derevyanko, S., Redyuk, A., Vergeles, S. & Turitsyn, S., 1 Jun 2018, In: APL Photonics. 3, 6, 13 p., 060801.

    Research output: Contribution to journal › Article › peer-review

  9. Interpretation models for data of metal-oxide gas sensors based on machine learning methods

    Kozmin, A. D. & Redyuk, A. A., 2024, In: Journal of Computational Technologies. 29, 4, p. 4-23 20 p.

    Research output: Contribution to journal › Article › peer-review

  10. Interchannel nonlinearity compensation using a perturbative machine learning technique

    Kozulin, I. A. & Redyuk, A. A., 15 Aug 2021, In: Optics Communications. 493, 127026.

    Research output: Contribution to journal › Article › peer-review

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