Machine Learning Techniques in Optical Communication

Zibar, Darko; Piels, Molly; Jones, Rasmus T.; Schaeffer, Christian

doi:10.1109/jlt.2015.2508502

Cited by 192 publications

(57 citation statements)

References 23 publications

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“…Accurately predicting the signal and noise power evolution of a long chain of un-flattened optical amplifiers for arbitrary transmit (TX) power profiles is difficult, as a small change in the TX power spectral density (PSD) or in the spectral link characteristics may cause a complicated evolution of signal and noise powers through the system, making it intractable to computationally solve the problem using analytical or numerical physics-based optical amplifier models. We therefore resort to machine learning [4] and build a deep neural network (DNN) as a digital twin of our optical fiber link. Once properly trained with experimental link data, the DNN allows for an off-line gradient-descent (GD) optimization whose optimized results are then verified experimentally.…”

Section: Introductionmentioning

confidence: 99%

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

Cho

Chandrasekhar

Sula

et al. 2020

J. Lightwave Technol.

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Section: Introductionmentioning

confidence: 99%

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

Cho

Chandrasekhar

Sula

et al. 2020

J. Lightwave Technol.

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“…The application of ML to physical layer use cases is mainly motivated by the presence of non-linear effects in optical fibers, which make analytical models inaccurate or even too complex. This has implications, e.g., on the performance predictions of optical communication systems, in terms of BER, quality factor (Q-factor) and also for signal demodulation [30], [31], [32].…”

Section: Motivation For Using Machine Learning In Optical Networmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

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485

226

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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.

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“…EM has been used in optical communications, such as signal detection [46], and signal nonlinear compensation [47], but to the best of our knowledge, it has not been used in optical networks yet.…”

Section: Activation Functionmentioning

confidence: 99%

Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

108

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With the rapid development of Internet and communication systems, both in services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication network, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML).Optical network, which plays an important role both in core and access network in communication networks, also faces great challenges of system complexity and the requirement of manual operations. To overcome the current limitations and address the issues of future optical networks, it is essential to deploy more intelligence capability to enable autonomous and flexible network operations. ML techniques are proved to have superiority on solving complex problems; and thus recently, ML techniques have been used for many optical network applications.In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical networks monitoring and survivability. The use cases are analyzed and compared according to the used ML techniques. Besides, a tutorial for ML applications is provided from the aspects of the introduction of common ML algorithms, paradigms of ML, and motivations of applying ML. Lastly, challenges and possible solutions of ML application in optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.

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Machine Learning Techniques in Optical Communication

Cited by 192 publications

References 23 publications

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Machine learning for intelligent optical networks: A comprehensive survey

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