Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals
Linear signal processing algorithms are effective in dealing with linear transmission channel
and linear signal detection, whereas the nonlinear signal processing algorithms, from the …
and linear signal detection, whereas the nonlinear signal processing algorithms, from the …
Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems
We investigate the application of dynamic deep neural networks for nonlinear equalization
in long haul transmission systems. Through extensive numerical analysis we identify their …
in long haul transmission systems. Through extensive numerical analysis we identify their …
Neural turbo equalization: Deep learning for fiber-optic nonlinearity compensation
Recently, data-driven approaches motivated by modern deep learning have been applied to
optical communications in place of traditional model-based counterparts. The application of …
optical communications in place of traditional model-based counterparts. The application of …
Optical performance monitoring in fiber-optic networks enabled by machine learning techniques
We review applications of machine learning (ML) in various aspects of optical
communications including optical performance monitoring, fiber nonlinearity compensation …
communications including optical performance monitoring, fiber nonlinearity compensation …
Machine learning methods for optical communication systems
Machine Learning Methods for Optical Communication Systems Page 1 SpW2F.3.pdf Advanced
Photonics Congress (IPR, Networks, NOMA, PS, Sensors, SPPCom) © OSA 2017 1 Machine …
Photonics Congress (IPR, Networks, NOMA, PS, Sensors, SPPCom) © OSA 2017 1 Machine …
Stochastic digital backpropagation
NV Irukulapati, H Wymeersch… - IEEE Transactions …, 2014 - ieeexplore.ieee.org
In this paper, we propose a novel detector for single-channel long-haul coherent optical
communications, termed stochastic digital backpropagation (SDBP), which takes into …
communications, termed stochastic digital backpropagation (SDBP), which takes into …
Eye diagram measurement-based joint modulation format, OSNR, ROF, and skew monitoring of coherent channel using deep learning
In this work, deep learning is used to monitor coherent channel performance with eye
diagram measurement. Experiments show that the proposed technique can determine the …
diagram measurement. Experiments show that the proposed technique can determine the …
Fiber nonlinearity equalization with multi-label deep learning scalable to high-order DP-QAM
We use deep neural network (DNN) to compensate for Kerr-induced nonlinearity in fiber-
optic communications. The proposed DNN is scalable to high-order modulations by …
optic communications. The proposed DNN is scalable to high-order modulations by …
NLIN mitigation using turbo equalization and an extended Kalman smoother
We develop a method for inter-channel nonlinear interference noise (NLIN) mitigation,
based on Turbo equalization and an extended Kalman smoother. The method exploits both …
based on Turbo equalization and an extended Kalman smoother. The method exploits both …
A New Twist on Low-Complexity Digital Backpropagation
This work proposes a novel low-complexity digital backpropagation (DBP) method, with the
goal of optimizing the trade-off between backpropagation accuracy and complexity. The …
goal of optimizing the trade-off between backpropagation accuracy and complexity. The …