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 …
Computational complexity evaluation of neural network applications in signal processing
In this paper, we provide a systematic approach for assessing and comparing the
computational complexity of neural network layers in digital signal processing. We provide …
computational complexity of neural network layers in digital signal processing. We provide …
Performance versus complexity study of neural network equalizers in coherent optical systems
We present the results of the comparative performance-versus-complexity analysis for the
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …
End-to-end deep learning of optical fiber communications
In this paper, we implement an optical fiber communication system as an end-to-end deep
neural network, including the complete chain of transmitter, channel model, and receiver …
neural network, including the complete chain of transmitter, channel model, and receiver …
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 …
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …
audience, ranging from optical research and engineering communities to computer science …
Physics-based deep learning for fiber-optic communication systems
We propose a new machine-learning approach for fiber-optic communication systems
whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our …
whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our …
Nonlinear interference mitigation via deep neural networks
Nonlinear Interference Mitigation via Deep Neural Networks Page 1 W3A.4.pdf OFC 2018 © OSA
2018 Nonlinear Interference Mitigation via Deep Neural Networks Christian Häger(1,2) and …
2018 Nonlinear Interference Mitigation via Deep Neural Networks Christian Häger(1,2) and …
Applying neural networks in optical communication systems: Possible pitfalls
We investigate the risk of overestimating the performance gain when applying neural
network-based receivers in systems with pseudorandom bit sequences or with limited …
network-based receivers in systems with pseudorandom bit sequences or with limited …
Performance limits in optical communications due to fiber nonlinearity
In this paper, we review the historical evolution of predictions of the performance of optical
communication systems. We will describe how such predictions were made from the outset …
communication systems. We will describe how such predictions were made from the outset …