Time-Domain Learned Digital Back-Propagation

Performance for optical fibre transmissions can be improved by digitally reversing the channel environment. When this is achieved by simulating short segment by separating the chromatic dispersion and Kerr nonlinearity, this is known as digital back-propagation (DBP). Time-domain DBP has the potential to decrease the complexity with respect to frequency domain algorithms. However, when using finer step in the algorithm, the accuracy of the individual smaller steps suffers. By adapting the chromatic dispersion filters of the individual steps to simulated or measured data this problem can be mitigated. Machine learning frameworks have enabled the gradient-descent style adaptation for large algorithms. This allows to adopt many dispersion filters to accurately represent the transmission in reverse. The proposed technique has been used in an experimental demonstration of learned time-domain DBP using a four channel 64-GBd dual-polarization 64-QAM signal transmission over a 10 span recirculating loop totalling 1014 km. The signal processing scheme consists of alternating finite impulse response filters with nonlinear phase shifts, where the filter coefficient were adapted using the experimental measurements. Performance gains to linear compensation in terms of signal-to-noise ratio improvements were comparable to those achieved with conventional frequency-domain DBP. Our experimental investigation shows the potential of digital signal processing techniques with learned parameters in improving the performance of high data rate long-haul optical fibre transmission systems

Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration

Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch''. One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance--complexity trade-offs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work.Comment: 10 pages, 5 figures. Author version of a paper published in the Journal of Lightwave Technology. OSA/IEEE copyright may appl

A survey on fiber nonlinearity compensation for 400 Gbps and beyond optical communication systems

Optical communication systems represent the backbone of modern communication networks. Since their deployment, different fiber technologies have been used to deal with optical fiber impairments such as dispersion-shifted fibers and dispersion-compensation fibers. In recent years, thanks to the introduction of coherent detection based systems, fiber impairments can be mitigated using digital signal processing (DSP) algorithms. Coherent systems are used in the current 100 Gbps wavelength-division multiplexing (WDM) standard technology. They allow the increase of spectral efficiency by using multi-level modulation formats, and are combined with DSP techniques to combat the linear fiber distortions. In addition to linear impairments, the next generation 400 Gbps/1 Tbps WDM systems are also more affected by the fiber nonlinearity due to the Kerr effect. At high input power, the fiber nonlinear effects become more important and their compensation is required to improve the transmission performance. Several approaches have been proposed to deal with the fiber nonlinearity. In this paper, after a brief description of the Kerr-induced nonlinear effects, a survey on the fiber nonlinearity compensation (NLC) techniques is provided. We focus on the well-known NLC techniques and discuss their performance, as well as their implementation and complexity. An extension of the inter-subcarrier nonlinear interference canceler approach is also proposed. A performance evaluation of the well-known NLC techniques and the proposed approach is provided in the context of Nyquist and super-Nyquist superchannel systems.Comment: Accepted in the IEEE Communications Surveys and Tutorial