Optical Communication Systems And Networking
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Advanced Optical Communication Systems and Networks
This resource provides the latest details on 5th generation photonic systems that can be readily applied to projects in the field. Moreover, the book provides valuable, time-saving tools for network simulation and modeling. It includes coverage of optical signal transmission systems and networks; a wide range of critical methods and techniques, such as MIMO (multiple-input and multiple-output) by employing spatial modes in few-mode and multicore optical fiber; OFDM (orthogonal frequency-division multiplexing) utilized to enhance the spectral efficiency and to enable elastic optical networking schemes; and advanced modulation and coding schemes to approach the Shannon's channel capacity limit. There are detailed discussions on the basic principles and applications of high-speed digital signal processing, as well as description of the most relevant post-detection compensation techniques
Modeling and Optimization of Optical Communication Networks
MODELING and OPTIMIZATION of OPTICAL COMMUNICATION NETWORKS Optical networks are an integral part of many of the technologies that we use every day. It is a constantly changing and evolving area, with new materials, processes, and applications coming online almost daily. This book provides a basis for discussing open principles, methods and research problems in the modeling of optical communication networks. It also provides a systematic overview of the state-of-the-art research efforts and potential research directions dealing with optical communication metworks. It also simultaneously focuses on extending the limits of currently used systems encompassing optical and wireless domains and explores novel research on wireless and optical techniques and systems, describing practical implementation activities, results and issues. A handbook on applications for both academia and industry, this exciting new volume includes detailed discussions on real-world case studies on trends and emerging technologies associated with modeling of optical communication networks. This book also describes several numerical models and algorithms for simulation and optimization of optical communication networks. Modeling and optimization presents several opportunities for automating operations and introducing intelligent decision making in network planning and in dynamic control and management of network resources, including issues like connection establishment, self-configuration, and self-optimization, through prediction and estimation by utilizing present network state and historical data. It focuses on extending the limits of currently used systems encompassing optical and wireless domains, and explores the latest developments in applications like photonics, high speed communication systems and networks, visible light communication, nano-photonics, wireless, and MIMO systems.
Machine Learning for Future Fiber-Optic Communication Systems
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 the 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 problems, and provide intelligent and efficient services to the end users. With 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 the recent popularity of machine learning (ML) concepts in modern optical communication networks and the 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, and more - 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