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Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications



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Autore: Masaracchia Antonino Visualizza persona
Titolo: Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications Visualizza cluster
Pubblicazione: Stevenage : , : Institution of Engineering & Technology, , 2024
©2025
Edizione: 1st ed.
Descrizione fisica: 1 online resource (270 pages)
Disciplina: 621.38456
Soggetto topico: 6G mobile communication systems
Artificial intelligence
Altri autori: NguyenKhoi Khac  
DuongTrung Q  
SharmaVishal  
Nota di contenuto: Contents -- Preface -- About the authors -- Part I. Introduction to machine learning and neural networks -- 1. Artificial intelligence, machine learning, and deep learning -- 2. Deep neural networks -- Part II. Deep reinforcement learning -- 3. Markov decision process -- 4. Value function approximation for continuous state-action space -- 5. Policy search methods for reinforcement learning -- 6. Actor-critic learning -- Part III. Deep reinforcement learning in UAV-assisted 6G communication -- 7. UAV-assisted 6G communications -- 8. Distributed deep deterministic policy gradient for power allocation control in UAV-to-UAV-based communications -- 9. Non-cooperative energy-efficient power allocation game in UAV-to-UAV communication: a multi-agent deep reinforcement learning approach -- 10. Real-time energy harvesting-aided scheduling in UAV-assisted D2D networks -- 11. 3D trajectory design and data collection in UAV-assisted networks -- Part IV. Deep reinforcement learning in reconfigurable intelligent surface-empowered 6G communications -- 12. RIS-assisted 6G communications -- 13. Real-time optimisation in RIS-assisted D2D communications -- 14. RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning -- 15. Multi-agent learning in networks supported by RIS and multi-UAVs -- Index
Sommario/riassunto: This co-authored book explores the many challenges arising from real-time and autonomous decision-making for 6G by covering crucial advanced signal control and real-time decision-making methods for UAV- and RIS-assisted 6G wireless communications including the serious constraints in real-time optimisation problems.
Titolo autorizzato: Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications  Visualizza cluster
ISBN: 1-83724-384-0
1-83953-642-X
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9911006720403321
Lo trovi qui: Univ. Federico II
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Serie: Telecommunications Series