Machine Learning for Future Fiber-Optic Communication Systems

Teaches machine learning methods and techniques that can be applied to future optical communication systems

Alan Pak Tao Lau (Edited by), Faisal Nadeem Khan (Edited by)

9780323852272, Elsevier Science

Paperback / softback, published 15 February 2022

402 pages
23.5 x 19 x 2.6 cm, 0.84 kg

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.

1. Background introduction of ML techniques for optical communications
2. 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

Subject Areas: Laser technology & holography [TTBL], Applied optics [TTB], Communications engineering / telecommunications [TJK]