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Optical nonlinear impairment compensation based on Deep Neural Network (DNN) for coherent modulation systems
2022
Periodicals of Engineering and Natural Sciences (PEN)
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Its performance is evaluated experimentally on coherent 65536-bit sequence length with 25 Gbaud single polarization 4-16-64 QAM with 50 and 120 Gb/s back-to-back measurements through using pre-distort ...
Algorithms of Artificial Intelligence (AI) are utilized to determine and resolve the deficiencies by learning from the receiving information itself. ...
In our proposed design, we will not use any of the digital processing techniques and deep learning based on DNN will be fully relied upon to compensate for linear and nonlinear impairments. ...
doi:10.21533/pen.v10i1.2662
fatcat:gmdphmnqnnbq5ff722mavawmta
(OFC 2020) Hybrid Learning Assisted Impairments Abstraction for Service Planning and Provisioning Over Multi-Domain Optical Networks
2020
Journal of Optical Communications and Networking
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The hybrid learning assisted abstraction framework aims to abstract the property of segmental links along the lightpath and combine them for endto-end performance evaluation. ...
The proposed abstraction strategy consists of a parametric and a non-parametric machine learning technique to allow control plane implementing impairments abstraction with different accessible data or ...
performance in three optical networks using deep learning. ...
doi:10.1364/jocn.403056
fatcat:u4aqqzvmibe57iwtmubacuddgq
Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies
2020
Science China Information Sciences
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From the link aspect, adaptive fiber nonlinearity compensation, optical monitoring performance and quality of transmission estimation are developed to monitor and alleviate the link-dependent signal impairments ...
With the aid of AI techniques, the optical networks can perform in a self-learning manner, with the "self-aware" of network status, the "self-adaptive" of network actions and control policies, and the ...
The former two technologies focus on the optical signals performance monitoring and enhancement, and QoT estimation focuses on the performance of lightpaths which are consist of several links. ...
doi:10.1007/s11432-020-2871-2
fatcat:vrxcbkn5zzcshh5a4an5hyayvm
Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication
[article]
2022
arXiv
pre-print
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Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. ...
The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link. ...
SKT acknowledges the support of EPSRC project TRANSNET. ...
arXiv:2204.07457v1
fatcat:7e6to5mobzcnrhnirpxgospvhy
AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
2020
Applied Sciences
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Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. ...
However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. ...
Table 1 . 1 Summary of the machine learning (ML)-based quality of transmission (QoT) modeling techniques discussed in Section 2.2. ...
doi:10.3390/app10010363
fatcat:cw7umjrohve7xlle757afypvhu
Machine Learning Applications for Short Reach Optical Communication
2022
Photonics
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One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). ...
With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 ...
OSNR and nonlinear noise power estimation for optical fiber communication systems using
LSTM based deep learning technique. Opt. Express 2018, 26, 21346–21357. [CrossRef]
98. ...
doi:10.3390/photonics9010030
fatcat:n436p2z4vvbhlltafonxxnai5y
Paper titles
2020
2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)
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Virtual Machines for Learning Cross-Site Scripting Countermeasures A Probability-Based Analytical Model Based on Deep Learning for Traffic Information Estimation A Proposal of Transmission Power Optimization ...
Analysis of Programming Training based on Data Mining and High-interaction Performance Evaluation of Hierarchical Slotted ALOHA for IoT Applications Performance Evaluation of TCP BBR and CUBIC TCP in ...
doi:10.1109/icce-taiwan49838.2020.9258179
fatcat:2eheaztzhncixhbvp7nrbzml4m
Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications
[article]
2020
arXiv
pre-print
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Our results show that, for fixed algorithm memory, the DSP based on deep learning achieves an improved BER performance, allowing to increase the reach of the system. ...
The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward ...
Optical Fiber Transmission Link
B. ...
arXiv:2005.08790v1
fatcat:gc2744w4nzawjhra4z3jqtdpya
Tracing the exact location of failures in underground optical networks using LSTM deep learning model
2021
Indian Journal of Science and Technology
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Hence, the motivation to design an intelligent predictive model based on the LSTM deep learning technique to predict the exact location of failure in underground optical networks. ...
Using deep learning predictive models to tracing faults has been deployed in many network and transmission systems such as routing, fault management, link optimization, modulation, etc. ...
The quality of transmission in optical systems was estimated by (20) , by using support vector machine and synthetic datasets. ...
doi:10.17485/ijst/v14i4.2008
fatcat:oxvquub5pbfj7i3udrrrhracvm
Toward Deployments of ML Applications in Optical Networks
2021
IEEE Photonics Technology Letters
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Index Terms-Transfer learning, optical networks, Quality of Transmission estimation.
I. ...
A quality of transmission (QoT) estimator based on support vector machines (SVM) reduced the essential computing time to evaluate the QoT of an established lightpath [5] . ...
doi:10.1109/lpt.2021.3074586
fatcat:wmw567pu3fgkznyf4jcg26bttq
Recent Developments on Elastic Optical Networks: A brief Survey
2022
American Journal of Electronics & Communication
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In this paper, we review some of the recent developments in elastic optical networks using machine learning, different routing schemes and protection approaches. ...
Though the study in this field is more than a decade long, some recent developments like inclusion of machine learning tools and new protection based approaches have emerged. ...
This framework was supported by a broker plane consisting of deep learning-based traffic forecasting tools. ...
doi:10.15864/ajec.3204
fatcat:cysylsxrkbc3foee6632rql5vi
GCCE 2020 Subject Index
2020
2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)
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by Instantaneous Spectral Estimation
Separation of Multiple Sound Sources in the Same Direction by Instantaneous Spectral Estimation
Sequence-To-One Neural Networks for Japanese Dialect Speech Classification ...
Research for Unique Venue of Cultural Properties Made by Bricks in Japan
Research on the Less Stress Acquisition Method for the Activity Information of Actual Residents Using the International Standard ...
Function and Beam Direction Control Based on Deep Learning and PID Control for Optical Wireless Power Transmission
U 3 7 A B C D E F G H I K L M N O P Q R S T U V W
Underwater Visible Light Communication ...
doi:10.1109/gcce50665.2020.9291796
fatcat:bmnnn7xnxrefhaneq262fe4i6u
5G New Radio: Dynamic Time Division Duplex Radio Resource Management Approaches
2021
IEEE Access
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Moreover, to achieve high traffic by using only the user estimated location, one of the supervised machine learning techniques known as the random forest algorithm is used for manipulating the relationships ...
Deep learning techniques can be adopted to help for determining the direction of arrivals (DOA) and channel estimations [35] . ...
doi:10.1109/access.2021.3104277
fatcat:s6xyfoefwnanzd7be642wleul4
Artificial intelligence (AI) methods in optical networks: A comprehensive survey
2018
Optical Switching and Networkning Journal
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The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation ...
of nonlinearities, and quality of transmission estimation. ...
Acknowledgment This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TEC2014-53071-C3-2-P, TEC2015-71932-REDT). ...
doi:10.1016/j.osn.2017.12.006
fatcat:i443tt6fv5g2jmvfe7kn57unbq
An overview of ML-based applications for next generation optical networks
2020
Science China Information Sciences
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Although many ML-based researches have emerged, the applications of ML techniques in optical networks still face challenges. ...
Fortunately, artificial intelligence (AI), especially machine learning (ML), is regarded as one of the most promising methods to overcome these shortcomings. ...
This problem can be easily resolved by using a more accurate and flexible model based on ML techniques. In [37] , the BER of the link is continuously monitored. ...
doi:10.1007/s11432-020-2874-y
fatcat:a5gc5nti5fcfjbkside7yxn75q
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