Jaroslaw E. Prilepsky

Aston University

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Sergei K. Turitsyn

Aston University

144

Pedro J. Freire

Aston University

Antonio Napoli

Infinera (United States)

Nelson Costa

Infinera (United States)

Morteza Kamalian-Kopae

Aston University

Bernhard Spinnler

Infineon Technologies (Germany)

Stanislav Derevyanko

Ben-Gurion University of the Negev

Anastasiia Vasylchenkova

Springer Nature (United Kingdom)

Sasipim Srivallapanondh

Aston University

Wolfgang Schairer

Infineon Technologies (Germany)

Cooperative Institutions

Aston University

Nokia (Germany)

Infineon Technologies (Germany)

Infinera (United States)

National Academy of Sciences of Ukraine

Russian Academy of Sciences

V. N. Karazin Kharkiv National University

University College London

Novosibirsk State University

B. Verkin Institute for Low Temperature Physics and Engineering of the National Academy of Sciences of Ukraine

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Experimentally characterized nonlinear fourier transform of a mode-locked fibre laser

Srikanth Sugavanam Morteza Kamalian-Kopae Junsong Peng Jaroslaw E. Prilepsky Sergei K. Turitsyn

Summary form only given. Fibre lasers are known to provide a rich tapestry of operational regimes, which can be attributed to the nonlinear nature of light dynamics in optical fibre at high powers, and the multidimensional system parameter space. Given their inherent complexity, identifying and discerning the underlying physical processes that gives rise to them still remains a formidable challenge. Here, for the first time in experiment, we show how the Nonlinear Fourier Transform (NFT) (see e.g. [1-3] and references therein) can be used as an effective tool for the identification and classification of lasing regimes. The NFT provides a framework for identification of coherent structures (nonlinear multi-soliton modes) embedded into dispersive radiation [2, 3].

Identification

10.1109/cleoe-eqec.2017.8087529

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Citations (5)

Neural-Network-Based Nonlinearity Equalizer for 128 GBaud Coherent Transcievers

Optical Fiber Communication Conference (OFC) 2022 (2021)

Vladislav Neskorniuk Fred Buchali Vinod Bajaj Sergei K. Turitsyn Jaroslaw E. Prilepsky

We propose an efficient neural-network-based equalization jointly compensating fiber and transceiver nonlinearities for high-symbol-rate coherent short-reach links. Providing about 0.9 dB extra SNR gain, it allows achieving experimentally the record single-channel 1.48 Tbps net rate over 240 km G.652 fiber.

Transceiver

Symbol rate

Gigabit

10.1364/ofc.2021.th1a.30

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Citations (2)

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

arXiv (Cornell University) (2022)

Vladislav Neskorniuk Andrea Carnio Domenico Marsella Sergei K. Turitsyn Jaroslaw E. Prilepsky

Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.

Autoencoder

Boltzmann machine

Symbol (formal)

QAM

10.48550/arxiv.2204.07457

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Citations (0)

On the Design of NFT-Based Communication Systems With Lumped Amplification

Journal of Lightwave Technology (2017)

Morteza Kamalian-Kopae Jaroslaw E. Prilepsky Son Thai Le Sergei K. Turitsyn

Nonlinear Fourier transform (NFT) based transmission technique relies on the integrability of the nonlinear Schrödinger equation (NLSE). However, the lossless NLSE is not directly applicable for the description of light evolution in fibre links with lumped amplification such as Erbium-doped fibre amplifier (EDFA) because of the nonuniform loss and gain evolution. In this case, the path-averaged model is usually applied as an approximation of the true NLSE model including the fibre loss. However, the inaccuracy of the lossless path-average model, even though being small, can also result in a notable performance degradation in NFT-based transmission systems. In this paper, we extend the theoretical approach, which was first proposed for solitons in EDFA systems, to the case of NFT-based systems to constructively diminish the aforementioned performance penalty. Based on the quantitative analysis of distortions due to the use of path-average model, we optimise the signal launch and detection points to minimise the models mismatch. Without loss of generality, we demonstrate how this approach works for the NFT systems that use continuous NFT spectrum modulation (vanishing signals) and NFT main spectrum modulation (periodic signals). Through numerical modelling, we quantify the corresponding improvements in system performance.

Modulation (music)

Transmission system

10.1109/jlt.2017.2775105

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Citations (25)

Full-Spectrum Periodic Nonlinear Fourier Transform Optical Communication Through Solving the Riemann-Hilbert Problem

Journal of Lightwave Technology (2020)

Morteza Kamalian-Kopae Anastasiia Vasylchenkova Dmitry Shepelsky Jaroslaw E. Prilepsky Sergei K. Turitsyn

In this article, for the first time, a full-spectrum periodic nonlinear Fourier transform (NFT)-based communication system with the inverse transformation at the transmitter performed by using the solution of Riemann-Hilbert problem (RHP), is proposed and studied. The entire control over the nonlinear spectrum rendered by our technique, where we operate with two qualitatively different components of this spectrum represented, correspondingly, in terms of the main spectrum and the phases, allows us to design a time-domain signal tailored to the characteristics of the transmission channel. In the heart of our system is the RHP-based signal processing utilised to generate the time-domain signal from the modulated nonlinear spectrum. This type of NFT processing leads to a computational complexity that scales linearly with the number of time-domain samples, and we can process signal samples in parallel. In this article, we suggest the way of getting an exactly periodic signal through the correctly formulated RHP, and present evidence of the analogy between band-limited (in ordinary Fourier sense) signals and finite-band (in RHP sense) signals. Also, for the first time, we explain how to modulate the phases of individual periodic nonlinear modes. The performance of our transmission system is evaluated through numerical simulations in terms of bit error rate and Q ² -factor dependencies on the transmission distance and power, and the results demonstrate the good potential of the approach.

SIGNAL (programming language)

10.1109/jlt.2020.2979322

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Citations (13)

Application of neural networks to determine the discrete spectrum of the direct Zakharov – Shabat problem

Quantum Electronics (2020)

Egor Sedov I. S. Chekhovskoy Jaroslaw E. Prilepsky М. П. Федорук

Abstract A neural network architecture is proposed to determine the number of solitons generated by random processes in optical wavelength-division multiplexed telecommunication systems with QPSK, 16-QAM, 64-QAM, and 1024-QAM modulation. The dependence of the prediction quality of a neural network with a special architecture on the number of soliton modes in the signal and the parameters of this signal is studied.

QAM

Modulation (music)

SIGNAL (programming language)

10.1070/qel17463

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Citations (6)

Optimal Launch and Detection Points for the NFT-based Communication System with Lumped Amplification

Morteza Kamalian-Kopae Jaroslaw E. Prilepsky Son ThaiLe Sergei K. Turitsyn

Applying the path-average theory, we define the optimal signal launch and detection points in nonlinear Fourier transform based communication systems with lumped amplification to minimise the deterministic signal distortions and to improve significantly the overall transmission performance.

SIGNAL (programming language)

Detection theory

10.1109/ecoc.2017.8346230

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Citations (2)

Machine learning for performance improvement of periodic NFT-based communication system

Oleksandr Kotlyar Morteza Kamalian-Kopae Jaroslaw E. Prilepsky M. Pankratova Sergei K. Turitsyn

We compare performance of several machine learning methods, including support vector machine, k-nearest neighbours, k-means clustering, and Gaussian mixture model, used for increasing transmission reach in the optical communication system based on the periodic nonlinear Fourier transform signal processing.

Transmission system

SIGNAL (programming language)

Kernel (algebra)

10.1049/cp.2019.1089

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Citations (6)

Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection

Sasipim Srivallapanondh Pedro J. Freire Bernhard Spinnler Nelson Costa Antonio Napoli

To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure. The latter shows 38% latency decrease, while impacting the Q-factor by only 0.5 dB.

Parallelizable manifold

Feed forward

10.23919/ofc49934.2023.10116725

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Citations (2)

Optical communication based on the periodic nonlinear Fourier transform signal processing

Morteza Kamalian-Kopae Jaroslaw E. Prilepsky Son Thai Le Sergei K. Turitsyn

In this work we introduce the periodic nonlinear Fourier transform (PNFT) and propose a proof-of-concept communication system based on it by using a simple waveform with known nonlinear spectrum (NS). We study the performance (addressing the bit-error-rate (BER), as a function of the propagation distance) of the transmission system based on the use of the PNFT processing method and show the benefits of the latter approach. By analysing our simulation results for the system with lumped amplification, we demonstrate the decent potential of the new processing method.

Multidimensional signal processing

SIGNAL (programming language)

10.1109/icp.2016.7510023

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Citations (8)