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.
1. Chapter One : Introduction to machine learning techniques: An optical communication's perspective - p. Page xiii 2. Chapter Two : Machine learning for long-haul optical systems - p. Page 40 3. Chapter Three : Machine learning for short reach optical fiber systems - p. Page 62 4. Chapter Four : Machine learning techniques for passive optical networks - p. Page 87 5. Chapter Five : End-to-end learning for fiber-optic communication systems - p. Page 112 6. Chapter Six : Deep learning techniques for optical monitoring - p. Page 136 7. Chapter Seven : Machine Learning methods for Quality-of-Transmission estimation - p. Page 185 8. Chapter Eight : Machine Learning for optical spectrum analysis - p. Page 221 9. Chapter Nine : Machine learning and data science for low-margin optical networks: The ins and outs of margin optimization - p. Page 279 10. Chapter Ten : Machine learning for network security management, attacks, and intrusions detection - p. Page 314 11. Chapter Eleven : Machine learning for design and optimization of photonic devices - p. Page 335 12. Index - p. Page 370
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.
1. Chapter One : Introduction to machine learning techniques: An optical communication's perspective - p. Page xiii 2. Chapter Two : Machine learning for long-haul optical systems - p. Page 40 3. Chapter Three : Machine learning for short reach optical fiber systems - p. Page 62 4. Chapter Four : Machine learning techniques for passive optical networks - p. Page 87 5. Chapter Five : End-to-end learning for fiber-optic communication systems - p. Page 112 6. Chapter Six : Deep learning techniques for optical monitoring - p. Page 136 7. Chapter Seven : Machine Learning methods for Quality-of-Transmission estimation - p. Page 185 8. Chapter Eight : Machine Learning for optical spectrum analysis - p. Page 221 9. Chapter Nine : Machine learning and data science for low-margin optical networks: The ins and outs of margin optimization - p. Page 279 10. Chapter Ten : Machine learning for network security management, attacks, and intrusions detection - p. Page 314 11. Chapter Eleven : Machine learning for design and optimization of photonic devices - p. Page 335 12. Index - p. Page 370
