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 …
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 …
Explainable artificial intelligence for 6G: Improving trust between human and machine
W Guo - IEEE Communications Magazine, 2020 - ieeexplore.ieee.org
As 5G mobile networks are bringing about global societal benefits, the design phase for 6G
has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery …
has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery …
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 …
Deep unfolding for communications systems: A survey and some new directions
A Balatsoukas-Stimming… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms
with tools from neural networks to efficiently solve a range of tasks in machine learning …
with tools from neural networks to efficiently solve a range of tasks in machine learning …
Physics-based deep learning for fiber-optic communication systems
C Häger, HD Pfister - IEEE Journal on Selected Areas in …, 2020 - ieeexplore.ieee.org
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 …
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 …
Compensation of fiber nonlinearities in digital coherent systems leveraging long short-term memory neural networks
We introduce for the first time the utilization of Long short-term memory (LSTM) neural
network architectures for the compensation of fiber nonlinearities in digital coherent systems …
network architectures for the compensation of fiber nonlinearities in digital coherent systems …