18,424 research outputs found
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. This approach enables the optimization of the
transceiver in a single end-to-end process. We illustrate the benefits of this
method by applying it to intensity modulation/direct detection (IM/DD) systems
and show that we can achieve bit error rates below the 6.7\% hard-decision
forward error correction (HD-FEC) threshold. We model all componentry of the
transmitter and receiver, as well as the fiber channel, and apply deep learning
to find transmitter and receiver configurations minimizing the symbol error
rate. We propose and verify in simulations a training method that yields robust
and flexible transceivers that allow---without reconfiguration---reliable
transmission over a large range of link dispersions. The results from
end-to-end deep learning are successfully verified for the first time in an
experiment. In particular, we achieve information rates of 42\,Gb/s below the
HD-FEC threshold at distances beyond 40\,km. We find that our results
outperform conventional IM/DD solutions based on 2 and 4 level pulse amplitude
modulation (PAM2/PAM4) with feedforward equalization (FFE) at the receiver. Our
study is the first step towards end-to-end deep learning-based optimization of
optical fiber communication systems.Comment: submitted to IEEE/OSA Journal of Lightwave Technolog
High-Cardinality Hybrid Shaping for 4D Modulation Formats in Optical Communications Optimized via End-to-End Learning
In this paper we carry out a joint optimization of probabilistic (PS) and
geometric shaping (GS) for four-dimensional (4D) modulation formats in
long-haul coherent wavelength division multiplexed (WDM) optical fiber
communications using an auto-encoder framework. We propose a 4D 10 bits/symbol
constellation which we obtained via end-to-end deep learning over the
split-step Fourier model of the fiber channel. The constellation achieved 13.6%
reach increase at a data rate of approximately 400 Gbits/second in comparison
to the ubiquitously employed polarization multiplexed 32-QAM format at a
forward error correction overhead of 20%.Comment: 5 pages, 3 figure
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
Experimental optical fiber communications link
An optical fiber communications link 1.5 kilometers in length was installed between the Interim Frequency Standard Test Facility and the Timing and Frequency Systems Research Laboratory at JPL. It is being used to develop optical fiber technology for use in the DSN and particularly for precise time and frequency distribution
Machine learning for fiber nonlinearity mitigation in long-haul coherent optical transmission systems
Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation
ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters
We consider time-domain digital backpropagation with chromatic dispersion
filters jointly optimized and quantized using machine-learning techniques.
Compared to the baseline implementations, we show improved BER performance and
>40% power dissipation reductions in 28-nm CMOS.Comment: 3 pages, 3 figures, updated reference list, added one sentence in the
result section for clarit
Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities
A new geometric shaping method is proposed, leveraging unsupervised machine
learning to optimize the constellation design. The learned constellation
mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a
simplified fiber channel model.Comment: 3 pages, 6 figures, submitted to ECOC 201
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