Nonlinear Interference Mitigation via Deep Neural Networks

A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32x100km fiber-optic link, the resulting "learned" DBP significantly reduces the complexity compared to conventional DBP implementations

ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters

We consider time-domain digital backpropagation with chromatic dispersion filters jointly optimized and quantized using machine-learning techniques. Compared to the baseline implementations, we show improved BER performance and >40% power dissipation reductions in 28-nm CMOS.Comment: 3 pages, 3 figures, updated reference list, added one sentence in the result section for clarit

Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning

We propose a low-complexity sub-banded DSP architecture for digital backpropagation where the walk-off effect is compensated using simple delay elements. For a simulated 96-Gbaud signal and 2500 km optical link, our method achieves a 2.8 dB SNR improvement over linear equalization.Comment: 3 pages, 3 figur

Machine learning for fiber nonlinearity mitigation in long-haul coherent optical transmission systems

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation

FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training

We design and implement an adaptive machine learning equalizer that alternates multiple linear and nonlinear computational layers on an FPGA. On-chip training via gradient backpropagation is shown to allow for real-time adaptation to time-varying channel impairments

Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices

We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resilience compared to previous inverse scattering methods, while highlighting challenges related to model overparameterization.Comment: Will be appeared in OFC 202

Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs hardware-friendly DBP and distributed PMD compensation with performance close to the PMD-free case.Comment: 3 pages, 2 figure

Antialiased transmitter-side digital backpropagation

Digital backpropagation (DBP) is an electronic scheme for compensating nonlinear distortions in fiber transmission systems. Due to the nonlinearity-induced spectral broadening, the data must be oversampled to avoid aliasing, which increases the complexity and power consumption of the scheme. In this work, we show that aliasing can alternatively be prevented by distributed antialiasing filters, at a lower complexity. We proposed a new modified split-step Fourier method (SSFM) with easy-To-implement low-pass filters (LPFs) in the linear steps to avoid aliasing due to spectral broadening. Both the forward fiber propagation and a transmitter-side DBP are simulated using the modified SSFM. High-order modulation formats such as 256-Ary quadrature-Amplitude-modulation (256-QAM) and 1024-QAM transmissions at 28 Gbaud and 64 Gbaud over 1000 km fiber are considered, and our results show that the complexity of the DBP can be reduced by up to 50%. The optimal bandwidth of the LPFs is studied for both forward propagation and the DBP