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.
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Table of Contents
1. Background introduction of ML techniques for optical communications2. ML techniques for long-haul systems
3. ML techniques for IM/DD systems
4. ML techniques for passive optical networks
5. ML for end-to-end learning of complete fiber-optic communication system
6. ML methods for QoT estimation and optical performance monitoring
7. ML-based adaptive network resources allocation, control and management
8. ML-assisted cognitive network fault protection and management
9. ML for cross-layer optimizations and automated network operation in SDNs
10. ML for network security management, and attacks and intrusions detection
11. ML for low-margin optical networking
12. ML for quantum optical communication systems
13. ML for intelligent testing and measurement equipment
14. ML for design and optimization of photonic devices and sub-systems
15. ML for channel coding
