Optical Fiber Communication Systems Based on End-to-End Deep Learning : (Invited Paper)

Karanov, Boris; Chagnon, Mathieu; Aref, Vahid; Lavery, Domaniç; Bayvel, Polina; Schmalen, Laurent

doi:10.1109/ipc47351.2020.9252544

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…Adjusting W , the SBRNN allows transmission below the 6.7% HD-FEC [22] at distances beyond 70 km -yielding > 20 km improvement over FFNN. Verified in experiments, the systems outperformed state-of-the-art DSP [6], [13], [14] . Conclusions This paper reviews the methods for end-to-end optimized optical fiber transmission.…”

Section: Transceiver Design and Performancementioning

confidence: 74%

“…The combination of artificial neural networks (ANNs), known as universal function approximators [1] , and deep learning [2] provides a framework for optimizing the system in a single end-to-end process -an idea first introduced for wireless communications [3]- [5] . The approach was quickly utilized also in optical fiber communications aiming at exploiting to a greater extent the potential for data transmission over nonlinear dispersive channels [6]- [14] . It consists in implementing the complete fiber-optic system as an end-toend computational graph using ANN-based transmitter and receiver.…”

Section: Introductionmentioning

confidence: 99%

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End-to-End Learning in Optical Fiber Communications: Concept and Transceiver Design

Karanov

Bayvel²,

Schmalen

2020

2020 European Conference on Optical Communications (ECOC)

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published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

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Section: Transceiver Design and Performancementioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

End-to-End Learning in Optical Fiber Communications: Concept and Transceiver Design

Karanov

Bayvel²,

Schmalen

2020

2020 European Conference on Optical Communications (ECOC)

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“…Simple linear regression and multiple linear regression are the major classification algorithms [27], [28]. One independent variable is present in simple linear regression.…”

Section: Linear Regressionmentioning

confidence: 99%

Machine learning-based QoT estimation over optical wireless communication

Sahu

Clement

2023

Preprint

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Fiber-optic communication and networks play an important role in communications technology. According to the last few decades, in this area, research development is growing rapidly. Machine learning (ML) algorithms for optical communications (OC) are certainly a hot topic in the current generation. To overcome the current limitation and different issues of fiber-optic communication and network, a machine learning (ML) algorithm is essential for us. Machine learning techniques are proof that it has superiority in solving complex problems. Machine learning algorithms generally focused on the education field, business organization, and health sectors. Currently, many researchers work in the optical communications area by using machine learning algorithms techniques. A machine learning algorithm is an emerging technology because it helps in the optical communication field for a better quality of service (QoS). According, to the first time, this work reviews machine learning for optical communication literature from a machine learning viewpoint. Only fiber-optic communication and network experts work on machine learning for optical networks, and they are not ML algorithms experts. This paper uses machine learning algorithms for calculating the quality of transmission (QoT) of light paths in optical 1 Springer Nature 2021 L A T E X template Article Title networks link. For better quality of transmission (QoT) estimation tools, it can show the performance analysis of machine learning-based algorithms by using such as bit error rate (BER), optical signal to noise ratio (OSNR), quality factor (Q-factor), blocking probability, and signal to noise ratio (SNR) data. This paper presents a novel concept of quality of transmission (QoT) based on the machine learning algorithm.

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“…An E2E wireless communication system was first proposed in [13], which does not require complex conventional communication modules, and the whole system is entirely composed of DNN. As a purely data-driven approach, it has attracted wide attention from scholars and has been studied in several other works [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. However, in the E2E wireless communication system proposed in [13], [16], and [17], the input information is represented by one-hot vectors, which have the disadvantage of carrying less information compared to random sequences of the same length.…”

Section: Research Progress Of End-to-end Wireless Communication Systemsmentioning

confidence: 99%

A Learning-Based End-to-End Wireless Communication System Utilizing a Deep Neural Network Channel Module

Wang

et al. 2023

IEEE Access

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The existing end-to-end (E2E) wireless communication systems require fewer communication modules and have a simple processing signal flow, compared to conventional wireless communication systems. However, in the absence of a differentiable channel model, it is impossible to train transmitters, used in such systems, which makes impossible to get optimal system performance. To solve this problem, an E2E wireless communication system, learned with conditional generative adversarial networks (CGANs) for channel modeling, has been proposed recently. Unfortunately, the CGAN training is prone to instability, slow convergence, and inaccurate channel modeling, which affects the system performance. To this end, a learning-based E2E wireless communication system, utilizing a deep neural network (DNN) channel module to model an unknown channel, is proposed in this paper. Simulation results show that the proposed DNN channel modeling has faster convergence, simpler network structure, and can reflect the behavior of real channels more accurately. In addition, the proposed E2E wireless communication system performs better, in terms of bit error rate (BER) and block error rate (BLER), than the E2E wireless communication system, using CGAN as unknown channel, and a traditional communication system, designed based on the prior knowledge of the channel. Compared to these two systems, at high signal-to-noise ratio (SNR) values, the proposed system can achieve a SNR gain of at least 2 dB, in communication scenarios involving frequency-selective multi-path channels. INDEX TERMSAutoencoder, end-to-end communication systems, deep learning, deep neural network (DNN) I. INTRODUCTION Wireless communication has played an important role in our daily life in recent years due to the rapid development of relevant technologies. In the fifth generation of mobile communications (5G), especially, many new technologies, such as massive multiple-input multiple-output (MIMO) [1], [2], beamforming [3], [4], and edge computing [5], were utilized, which has enabled the expansion of wireless communication applications to industrial, medical, and educational fields. The conventional wireless communication system architecture consists of multiple signal processing sub-modules, shown in Fig. 1. Message , which must be sent to the other side, is transformed by the transmitter into a suitable (encoded and modulated) signal , which after passing through the channel, becomes signal, which arrives at the receiver and is converted into message by performing opposite procedures to that of the transmitter. The received message is generally (slightly) different from the originally sent message due to errors occurring in it,

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Optical Fiber Communication Systems Based on End-to-End Deep Learning : (Invited Paper)

Cited by 7 publications

References 9 publications

End-to-End Learning in Optical Fiber Communications: Concept and Transceiver Design

End-to-End Learning in Optical Fiber Communications: Concept and Transceiver Design

Machine learning-based QoT estimation over optical wireless communication

A Learning-Based End-to-End Wireless Communication System Utilizing a Deep Neural Network Channel Module

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