417 research outputs found
Machine learning-based Raman amplifier design
A multi-layer neural network is employed to learn the mapping between Raman
gain profile and pump powers and wavelengths. The learned model predicts with
high-accuracy, low-latency and low-complexity the pumping setup for any gain
profile.Comment: conferenc
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 alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
We report a neural-network based erbium-doped fiber amplifier (EDFA) gain
model built from experimental measurements. The model shows low gain-prediction
error for both the same device used for training (MSE 0.04 dB) and
different physical units of the same make (generalization MSE 0.06
dB)
Building a digital twin of EDFA: a grey-box modeling approach
To enable intelligent and self-driving optical networks, high-accuracy
physical layer models are required. The dynamic wavelength-dependent gain
effects of non-constant-pump erbium-doped fiber amplifiers (EDFAs) remain a
crucial problem in terms of modeling, as it determines optical-to-signal noise
ratio as well as the magnitude of fiber nonlinearities. Black-box data-driven
models have been widely studied, but it requires a large size of data for
training and suffers from poor generalizability. In this paper, we derive the
gain spectra of EDFAs as a simple univariable linear function, and then based
on it we propose a grey-box EDFA gain modeling scheme. Experimental results
show that for both automatic gain control (AGC) and automatic power control
(APC) EDFAs, our model built with 8 data samples can achieve better performance
than the neural network (NN) based model built with 900 data samples, which
means the required data size for modeling can be reduced by at least two orders
of magnitude. Moreover, in the experiment the proposed model demonstrates
superior generalizability to unseen scenarios since it is based on the
underlying physics of EDFAs. The results indicate that building a customized
digital twin of each EDFA in optical networks become feasible, which is
essential especially for next generation multi-band network operations
Gain profile characterization and modelling for an accurate EDFA abstraction and control
Relying on a two-measurement characterization phase, a gain profile model for
dual-stage EDFAs is presented and validated in full spectral load condition. It
precisely reproduces the EDFA dynamics varying the target gain and tilts
parameters as shown experimentally on two commercial items from different
vendors
Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
Cascades of a machine learning-based EDFA gain model trained on a single
physical device and a fully differentiable stimulated Raman scattering fiber
model are used to predict and optimize the power profile at the output of an
experimental multi-span fully-loaded C-band optical communication system
Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain
We present a novel ML framework for modeling the wavelength-dependent gain of
multiple EDFAs, based on semi-supervised, self-normalizing neural networks,
enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open
Ireland and COSMOS testbeds show high-accuracy transfer-learning even when
operated across different amplifier types.Comment: This paper was accepted for the European Conference on Optical
Communications (ECOC) 2023, this version is a pre-prin
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