Searcharxiv⌕ Search

arXiv subjects

Mikhail Fedoruk

Publications and source records attributed to Mikhail Fedoruk.

11 recordsLinked to original sources
arXiv2026

Fast eight-order Pade schemes based on Chebyshev polynomials for direct Zakharov-Shabat problem

In this work, we construct fast eighth-order Pade schemes for the direct Zakharov-Shabat scattering problem. The schemes are based on an eighth-order exponential integrator obtained from the Magnus expansion. A direct extension of the conventional fast Pade representation to the eighth-order case leads to insufficiently accurate fast variants in the considered tests. To overcome this difficulty, we reformulate the spectral dependence on a finite real interval using the Joukowski mapping and represent the local numerators and denominators in the Chebyshev polynomial basis. This makes it possible to construct the global transition matrix by a fast product tree while retaining a compact polynomial representation. Numerical experiments for chirped hyperbolic secant potentials with both signs of dispersion show that the proposed Chebyshev-based fast schemes substantially improve the accuracy of the corresponding direct fast variants and can be used for efficient computation of the continuous nonlinear spectrum.

math.NA↗
arXiv2026

Reservoir computing based on multicore fibers

Photonic reservoir computing offers a hardware-efficient route to processing temporal and sequential data, but delay-based implementations often rely heavily on temporal multiplexing, where long temporal masks are required to generate a sufficiently rich reservoir state. Here we show numerically that the spatial degrees of freedom of an active multicore fiber placed inside a delayed optical feedback loop can reduce this dependence on serial temporal encoding. The input signal is encoded by temporal and spatial masks, the pump distribution across the cores controls the reservoir operating point through the core-dependent effective gain and saturation energy, and the detected core intensities serve as readout features for a single trained linear layer. The system is modeled by linearly coupled nonlinear Schr\"odinger equations with saturable gain and solved using a split-step Fourier method. On the Mackey-Glass one-step-ahead prediction benchmark, a seven-core reservoir with equal temporal masks reduces the validation normalized root mean square error from 0.5956 for the single-core baseline to 0.0651 at a modulation rate of 40 GHz. At 1 GHz, spatial-only encoding reaches an error of 0.0323 using one temporal sample per symbol and no temporal mask. These results show that an active multicore fiber can provide both parallel readout channels and a tunable nonlinear transformation, offering a route to photonic reservoirs with reduced reliance on temporal multiplexing.

physics.optics↗
arXiv2026

ML-assisted Subband Learned Digital Backpropagation for Nonlinearity Compensation in Wideband Optical Systems

Digital backpropagation (DBP) is one of the most effective techniques for compensating nonlinear distortions in coherent optical fiber communication systems. However, its practical application to wideband transmission remains limited by high computational complexity caused by large channel memory and the requirement for fine spatial discretization. In this work, we propose a subband-based learned digital backpropagation (SbL-DBP) framework for wideband optical transmission systems. The received signal is decomposed into multiple subbands, enabling independent frequency-domain compensation of the chromatic dispersion with reduced effective channel memory and lower computational complexity. Nonlinear intra- and inter-subband interactions are addressed in the time domain using a trainable multi-input multi-output filtering structure. The parameters of the proposed framework are jointly optimized using end-to-end gradient-based learning. In addition, sparsification techniques are employed to remove insignificant coefficients and further reduce computational complexity. Numerical simulations of an 11$\times$40~Gbaud WDM RRC-16QAM 20$\times$100 km transmission system demonstrate that the proposed method provides a superior performance--complexity trade-off compared to conventional DBP and enhanced DBP. In the low- and medium-complexity regimes, SbL-DBP provides higher signal-to-noise ratio gains while requiring fewer propagation steps.

physics.optics↗
arXiv2024

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

Nonlinear signal distortions are one of the primary factors limiting the capacity and reach of optical transmission systems. Currently, several approaches exist for compensating nonlinear distortions, but for practical implementation, algorithms must be simultaneously accurate, fast, and robust against various interferences. One established approach involves applying perturbation theory methods to the nonlinear Schr\"{o}dinger equation, which enables the determination of the relation between transmitted and received symbols. In most studies, gradient methods are used to find perturbation coefficients by minimizing the mean squared error between symbols. However, the main parameter characterizing the quality of information transmission is the bit error rate. We propose a modification of the conventional perturbation-based approach for fiber nonlinearity compensation in the form of a two-stage scheme for calculating perturbation coefficients. In the first stage, the coefficients are computed using a convolutional neural network by minimizing the mean squared error. In the second stage, the obtained solution is used as an initial approximation for minimizing the bit error rate using the particle swarm optimization method. In numerical experiments, using the nonlinearity compensation algorithm based on the proposed scheme, we achieved a 0.8~dB gain in the signal-to-noise ratio for a 16QAM 20$\times$100 km link with a channel rate of 267~Gbit/s and demonstrated improved accuracy compared to the single-stage scheme. We estimated computational complexity of the algorithm and demonstrated the relation between its complexity and accuracy. Additionally, we developed a method for learning perturbation coefficients without relying on ideal symbols from the transmitter, instead using the received symbols after hard decision detection.

physics.optics↗
arXiv2024

High-Order Block Toeplitz Inner-Bordering method for solving the Gelfand-Levitan-Marchenko equation

We propose a high precision algorithm for solving the Gelfand-Levitan-Marchenko equation. The algorithm is based on the block version of the Toeplitz Inner-Bordering algorithm of Levinson's type. To approximate integrals, we use the high-precision one-sided and two-sided Gregory quadrature formulas. Also we use the Woodbury formula to construct a computational algorithm. This makes it possible to use the almost Toeplitz structure of the matrices for the fast calculations.

math.NA↗
arXiv2021

Processing of optical signals by "surgical" methods for the Gelfand-Levitan-Marchenko equation

We propose a new method for solving the Gelfand-Levitan-Marchenko equation (GLME) based on the block version of the Toeplitz Inner-Bordering (TIB) with an arbitrary point to start the calculation. This makes it possible to find solutions of the GLME at an arbitrary point with a cutoff of the matrix coefficient, which allows to avoid the occurrence of numerical instability and to perform calculations for soliton solutions spaced apart in the time domain. Using an example of two solitons, we demonstrate our method and its range of applicability. An example of eight solitons shows how the method can be applied to a more complex signal configuration.

math.NA↗
arXiv2020

Fast sixth-order algorithm based on the generalized Cayley transform for the Zakharov-Shabat system in optical applications

Based on the generalized Cayley transform, a family of conservative one-step schemes of the sixth order of accuracy for the Zakharov-Shabat system is constructed. The exponential integrator is a special case. Schemes based on rational approximation allow the use of fast algorithms to solve the initial problem for a large number of values of the spectral parameter.

math.NA↗
arXiv2020

Introducing phase jump tracking -- a fast method for eigenvalue evaluation of the direct Zakharov-Shabat problem

We propose a new method for finding discrete eigenvalues for the direct Zakharov-Shabat problem, based on moving in the complex plane along the argument jumps of the function $a(ζ)$, the localization of which does not require great accuracy. It allows to find all discrete eigenvalues taking into account their multiplicity faster than matrix methods and contour integrals. The method shows significant advantage over other methods when calculating a large discrete spectrum, both in speed and accuracy.

math.NA↗
arXiv2019

Fast Computation of the Direct Scattering Transform by Fourth Order Conservative Multi-Exponential Scheme

A fourth-order multi-exponential scheme is proposed for the Zakharov-Shabat system. The scheme represents a product of 13 exponential operators. The construction of the scheme is based on a fourth-order three-exponential scheme, which contains only one exponent with a spectral parameter. This exponent is factorized to the fourth-order with the Suzuki formula of 11 exponents. The obtained scheme allows the use of a fast algorithm in calculating the initial problem for a large number of spectral parameters and conserves the quadratic invariant exactly for real spectral parameters.

math.NA↗
arXiv2019

Exponential Fourth Order Schemes for Direct Zakharov-Shabat problem

We propose two finite-difference algorithms of fourth order of accuracy for solving the initial problem of the Zakharov-Shabat system. Both schemes have the exponential form and conserve quadratic invariant of Zakharov-Shabat system. The second scheme contains the spectral parameter in exponent only and allows to apply the fast computational algorithm.

math.NA↗