E-Books →Machine Learning for Future Fiber-Optic Communication Systems

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 rolePresents 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 moreCovers 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