Machine Learning for Future Fiber-Optic Communication Systems

Name: Machine Learning for Future Fiber-Optic Communication Systems
Brand: Elsevier Books
SKU: 9780323852272
Price: 149.37 EUR
Availability: InStock

Автор: Alan Lau, Faisal Khan

Език:

Корици: С меки корици

Издател: Elsevier Books

Наличност: Външен склад

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149.37 € 292.13 лв

Machine learning (ML) is being hailed as a new direction of innovation to transform future optical c...

Информация за книгата

Автор

Alan Lau, Faisal Khan

Език

Английски език

Корици

Книга - С меки корици

Издадена

2022

страници

402

EAN

9780323852272

Enbook ID

37327845

Издател

Elsevier Books

Теглоt

840

Размери

191 x 235

Пълно описание

Machine learning (ML) is being hailed as a new direction of innovation to transform future optical communication systems. Signal processing paradigms based on ML are being considered to solve certain critical problems in optical networks that cannot be easily tackled using conventional approaches. Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning concepts and techniques applied to key areas within optical communications and networking, reflecting state-of-the-art research and industrial practices. The book gives knowledge and insights into the role machine learning-based mechanisms will soon play in the future realization of intelligent optical network infrastructures that can manage and monitor themselves, diagnose and resolve their problems, and provide intelligent and efficient services to the end users. With an up-to-date coverage and extensive treatment of various important topics related to machine learning for fiber-optic communication systems, this book is an invaluable reference for photonics researchers and engineers.. It is also a very suitable text for graduate students interested in ML-based signal processing and networking. Discusses the reasons behind recent popularity of machine learning (ML) concepts in modern optical communication networks and why/where/how ML can play a unique role Presents fundamental ML techniques like artificial neural networks (ANNs), support vector machines (SVMs), K-means clustering, expectation-maximization (EM) algorithm, principal component analysis (PCA), independent component analysis (ICA), reinforcement learning, etc., from communication theory and signal processing perspectives Covers advanced deep learning (DL) methods such as deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs) Individual chapters focus on ML applications in key areas of optical communications and networking discussing the decisive role played by ML in solving the underlying problems as well as highlighting the future research directions

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