Anastasiia Vasylchenkova

A closed-form formula for the nonlinear interference (NLI) estimation using the Gaussian noise (GN) model in the presence of inter-channel stimulated Raman scattering (ISRS) is derived. The formula enables accurate estimation of the NLI evolution along any portion of the fibre span together with arbitrary values of optical fibre losses. The formula also accounts for wavelength-dependent fibre parameters, variable modulation formats and launch power profiles. The formula is suitable for ultra-wideband (UWB) optical transmission systems and its accuracy is assessed for a system with 20 THz optical bandwidth over the entire S-, C-, and L- band through comparison with numerical integration of the ISRS GN model and split-step Fourier method (SSFM) simulations in point-to-point transmission and inline NLI estimation scenarios.

We demonstrate the real-time performance of an adaptive intelligent transceiver, tailoring the constellation shape to the transmission system by iteratively maximising the information throughput, quantified by the GMI.

In ultrawideband transmission, the overall noise comes from the amplification, fiber properties at different wavelengths, and stimulated Raman scattering, and its impact on channels across transmission bands is different. This requires a range of methods to mitigate the noise impact. Performing channel-wise power pre-emphasis and constellation shaping, one can compensate for the noise tilt and attain maximum throughput. In this work, we study the trade-off between the goals of maximizing the total throughput and leveling the transmission quality for different channels. We use an analytical model for multi-variable optimization and identify the penalty from constraining the mutual information variation.

Ultra-wideband transmission is a promising, cost-effective solution to meet the increasing demand for data traffic in optical fiber systems. However, system performance and quality of transmission (QoT) are limited by fiber nonlinearity, in particular, interchannel stimulated Raman scattering (ISRS), leading to a power transfer from short to long wavelengths. As the result, per-channel launch power optimization is required to maximize the system throughput. In this paper, we investigate how the transceiver noise and launch power optimization impact the total throughput and the per-channel QoT for transmission bandwidths of up to 20 THz, using the S-, C-, and L-bands. To measure the gains through the optimum launch power profile, a spectrally uniform launch power is used as a baseline. Through experimental analysis and theoretical modeling, the main limitations constraining the achieved data throughput of 178 Tb/s over a continuous bandwidth of 16.83 THz and 40 km were investigated. The impact of the launch power optimization and the transceiver constrained signal-to-noise ratio (SNR) are analyzed and compared, and the approaches to overcome data throughput limitations are considered. An extensive theoretical investigation for different system configurations is described, demonstrating the trade-off between ISRS impact and transceiver noise. The former degrades the QoT and increases the gains in performance obtained by optimizing the launch power, while the latter reduces these gains and the SNR variation across the bandwidth, with a major impact over short distances.

Analytical models for the estimation of nonlinearity-caused impairments enable effective maximisation of total data throughput. We demonstrate a 3 times improvement in per-channel mutual information when data is redistributed from the L-band to the S-band.

We demonstrate that in ultrawideband transmisison over 20THz, the added value of joint probabilistic shaping and power pre-emphasis is a factor of two higher than their individual contributions and gives a 20% increase in total mutual information.

We evaluate improvement in the performance of the optical transmission systems operating with the continuous nonlinear Fourier spectrum by the artificial neural network equalisers installed at the receiver end. We propose here a novel equaliser designs based on bidirectional long short-term memory (BLSTM) gated recurrent neural network and compare their performance with the equaliser based on several fully connected layers. The proposed approach accounts for the correlations between different nonlinear spectral components. The application of BLSTM equaliser leads to a 16x improvement in terms of bit-error rate (BER) compared to the non-equalised case. The proposed equaliser makes it possible to reach the data rate of 170 Gbit/s for one polarisation conventional nonlinear Fourier transform (NFT) based system at 1000 km distance. We show that our new BLSTM equalisers significantly outperform the previously proposed scheme based on a feed-forward fully connected neural network. Moreover, we demonstrate that by adding a 1D convolutional layer for the data pre-processing before BLSTM recurrent layers, we can further enhance the performance of the BLSTM equaliser, reaching 23x BER improvement for the 170 Gbit/s system over 1000 km, staying below the 7% forward error correction hard decision threshold (HD-FEC).

We propose a method to improve the performance of the nonlinear Fourier transform (NFT)-based optical transmission system by applying the neural network post-processing of the nonlinear spectrum at the receiver. We demonstrate through numerical modeling about one order of magnitude bit error rate improvement and compare this method with machine learning processing based on the classification of the received symbols. The proposed approach also offers a way to improve numerical accuracy of the inverse NFT; therefore, it can find a range of applications beyond optical communications.

We address the properties of nonlinear-Fourier-transform (NFT)-based fiber-optic communications systems and, particularly, study how the presence of noise deteriorates the performance of these systems. Similarly to the case of linear Fourier modes evolving independently under the action of chromatic dispersion alone, NFT-based systems employ so-called “nonlinear modes,” forming a nonlinear spectrum, as data carriers, and these nonlinear modes evolve independently in an uncoupled manner under the joint action of nonlinearity and dispersion. However, the influence of amplified-spontaneous-emission (ASE) noise on these nonlinear modes is still relatively poorly studied. In this paper, dealing with a continuous nonlinear spectrum, we scrutinize the properties of the effective noise emerging in the nonlinear Fourier domain. We also show that in the transmission stages, where the signal peak power is relatively high (e.g., at the receiver, in back-to-back transmission, or at short distances), the performance of an NFT system is mostly degraded not by the inline ASE noise, but by the imperfections of the digital sampling and the forward and backward NFT algorithms, i.e., by the NFT processing noise.