Buch, Englisch, 402 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 840 g
Buch, Englisch, 402 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 840 g
ISBN: 978-0-323-85227-2
Verlag: William Andrew Publishing
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.
Zielgruppe
R&D engineers in optical communications; University researchers in photonics
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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