An overview on application of machine learning techniques in optical networks
Today's telecommunication networks have become sources of enormous amounts of widely
heterogeneous data. This information can be retrieved from network traffic traces, network …
heterogeneous data. This information can be retrieved from network traffic traces, network …
An optical communication's perspective on machine learning and its applications
Machine learning (ML) has disrupted a wide range of science and engineering disciplines in
recent years. ML applications in optical communications and networking are also gaining …
recent years. ML applications in optical communications and networking are also gaining …
Field and lab experimental demonstration of nonlinear impairment compensation using neural networks
Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance
fiber optic transmission systems. Nonlinear impairments are determined by the signal …
fiber optic transmission systems. Nonlinear impairments are determined by the signal …
Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
In long-haul optical communication systems, compensating nonlinear effects through digital
signal processing (DSP) is difficult due to intractable interactions between Kerr nonlinearity …
signal processing (DSP) is difficult due to intractable interactions between Kerr nonlinearity …
Intelligent constellation diagram analyzer using convolutional neural network-based deep learning
An intelligent constellation diagram analyzer is proposed to implement both modulation
format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using …
format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using …
Modulation format recognition and OSNR estimation using CNN-based deep learning
D Wang, M Zhang, Z Li, J Li, M Fu… - IEEE Photonics …, 2017 - ieeexplore.ieee.org
An intelligent eye-diagram analyzer is proposed to implement both modulation format
recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using a convolution …
recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using a convolution …
Machine learning techniques in optical communication
Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and
nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo …
nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo …
Multiband carrierless amplitude phase modulation for high capacity optical data links
Short range optical data links are experiencing bandwidth limitations making it very
challenging to cope with the growing data transmission capacity demands. Parallel optics …
challenging to cope with the growing data transmission capacity demands. Parallel optics …
Gaussian kernel-aided deep neural network equalizer utilized in underwater PAM8 visible light communication system
In this paper, we demonstrate a novel Gaussian kernel-aided deep neural network (GK-
DNN) equalizer that can effectively compensate for the high nonlinear distortion of …
DNN) equalizer that can effectively compensate for the high nonlinear distortion of …
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 …