Signal-Noise Interaction in Optical-Fiber Communication Systems Employing Nonlinear Frequency-Division Multiplexing

Pankratova, Maryna; Vasylchenkova, Anastasiia; Derevyanko, Stanislav A.; Chichkov, Nikolai B.; Prilepsky, Jaroslaw E.

doi:10.1103/physrevapplied.13.054021

Cited by 40 publications

(21 citation statements)

References 43 publications

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“…These modes stay de-coupled along the evolution, propagating in an efficiently linear manner down the optical fibre in the presence of Kerr nonlinearity. 8,9 But similar to the DBP-type methods, the signal processing based on the NFT algorithms suffers from the large numerical complexity, power-sensitive numerical errors, 13 and complicated signal-dependent noise affecting our NF information carriers along their evolution. 10,[13][14][15] We note that up to the moment, in addition to a multitude of theoretical studies, 9,15 there exists a number of experimental demonstrations of NFT-based transmission systems, 16 including recently introduced efficacious b-modulation method, 17,18 that incorporates the continuous NF spectrum part.…”

Section: Introductionmentioning

confidence: 99%

Fixed-point realization of fast nonlinear Fourier transform algorithm for FPGA implementation of optical data processing

Vasylchenkova

Salnikov

Karaman

et al. 2021

Nonlinear Optics and Applications XII

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The nonlinear Fourier transform (NFT) based signal processing has attracted considerable attention as a promising tool for fibre nonlinearity mitigation in optical transmission. However, the mathematical complexity of NFT algorithms and the noticeable distinction of the latter from the "conventional" (Fourier-based) methods make it difficult to adapt this approach for practical applications. In our work, we demonstrate a hardware implementation of the fast direct NFT operation: it is used to map the optical signal onto its nonlinear Fourier spectrum, i.e. to demodulate the data. The main component of the algorithm is the matrix-multiplier unit, implemented on field-programmable gate arrays (FPGA) and used in our study for the estimation of required hardware resources. To design the best performing implementation in limited resources, we carry out the processing accuracy analysis to estimate the optimal bit width. The fast NFT algorithm that we analyse, is based on the FFT, which leads to the O(N log 2 2 N ) method's complexity for the signal consisting of N samples. Our analysis revealed the significant demand in DSP blocks on the used board, which is caused by the complex-valued matrix operations and FFTs. Nevertheless, it seems to be possible to utilise further the parallelisation of our NFT-processing implementation for the more efficient NFT hardware realisation.

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Section: Introductionmentioning

confidence: 99%

Fixed-point realization of fast nonlinear Fourier transform algorithm for FPGA implementation of optical data processing

Vasylchenkova

Salnikov

Karaman

et al. 2021

Nonlinear Optics and Applications XII

Self Cite

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“…Optical noise inevitably arising during the amplification process 9 is one of the key challenges in optical communications. The noise results in random NF spectrum disturbances 32,33 , imposing limits on the NFDM transmission quality. Thus, in our current work, we analyse the capability of a neural network (NN) to denoise the NF spectra.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for computing the continuous nonlinear Fourier spectrum in focusing nonlinear Schrodinger equation: NFT-Net

Sedov

Freire

Seredin

et al. 2021

Preprint

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We combine the nonlinear Fourier transform (NFT) signal processing with machine learning methods for solving the direct spectral problem associated with the nonlinear Schrödinger equation. The latter is one of the core nonlinear science models emerging in a range of applications. Our focus is on the unexplored problem of computing the continuous nonlinear Fourier spectrum associated with decaying profiles, using a specially-structured deep neural network which we coined NFT-Net. The Bayesian optimisation is utilised to find the optimal neural network architecture. The benefits of using the NFT-Net as compared to the conventional numerical NFT methods becomes evident when we deal with noise-corrupted signals, where the neural networks-based processing results in effective noise suppression. This advantage becomes more pronounced when the noise level is sufficiently high, and we train the neural network on the noise-corrupted field profiles. The maximum restoration quality corresponds to the case where the signal-to-noise ratio of the training data coincides with that of the validation signals. Finally, we also demonstrate that the NFT b-coefficient important for optical communication applications can be recovered with high accuracy and denoised by the neural network with the same architecture.

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“…where d is the distance between the transmitting end and receiving end, which means the signal loss is extensively high for long distances. [7][8][9] By considering such issues based on the replacement of copper traces, a tradeoff to be achieved among the above discussed aspects which can maintain high communication efficiency with improved reliability.…”

Section: Introductionmentioning

confidence: 99%

Zenneck surface wave interconnect with encircle routing for effective inter chip communication

Penna

Shankar

Keshavamurthy

et al. 2021

Int J RF Microw Comput Aided Eng

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Generally, copper traces are widely used to provide communication among integrated circuits (IC's) of printed circuit board (PCB). In case of high rate data transfer, systems or traces may be damaged due to trace resistivity. In order to overcome this issue, surface wave phenomenon based channel/ waveguide is introduced for IC communication in this work. Here, high frequency communication is possible due to both conductivity and dielectric behaviors of surface waves. Use of Zenneck surface instead of copper traces with Encircle Routing (ER) based channel assignment helps to achieve efficient IC communication. High rate of data transfer with low channel dispersion are major benefits of ER based Zenneck surface wave communication.Matlab working platform is used for design analysis by means of attenuation, field decay, skin depth, channel dispersion, power saving rate and signal voltage gain. Also, performance analysis is taken within the frequency range of 50 to 500 GHz to explore a deterministic channel communication between the IC's as an alternate of copper traces.

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Signal-Noise Interaction in Optical-Fiber Communication Systems Employing Nonlinear Frequency-Division Multiplexing

Cited by 40 publications

References 43 publications

Fixed-point realization of fast nonlinear Fourier transform algorithm for FPGA implementation of optical data processing

Fixed-point realization of fast nonlinear Fourier transform algorithm for FPGA implementation of optical data processing

Neural networks for computing the continuous nonlinear Fourier spectrum in focusing nonlinear Schrodinger equation: NFT-Net

Zenneck surface wave interconnect with encircle routing for effective inter chip communication

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