01552nam 2200457Ia 450 991069964890332120230902161633.0(CKB)5470000002404448(OCoLC)706072243(EXLCZ)99547000000240444820110308d2010 ua 0engurmn|||||||||txtrdacontentcrdamediacrrdacarrierClosed Brayton Cycle power conversion unit for fission surface power, phase I final report[electronic resource] /Robert L. FullerCleveland, Ohio :National Aeronautics and Space Administration, Glenn Research Center,[2010]1 online resource (v, 64 pages) illustrationsNASA/CR-2010-215673Title from title screen (viewed on March 7, 2011)."June 2010."NASA contractor report ;NASA CR-215673.Brayton cyclenasatClosed cyclesnasatFissionnasatPower convertersnasatFluid flownasatBrayton cycle.Closed cycles.Fission.Power converters.Fluid flow.Fuller Robert L45494NASA Glenn Research Center.GPOGPOBOOK9910699648903321Closed Brayton Cycle power conversion unit for fission surface power, phase I final report3507658UNINA03391nam 22005293 450 991100672040332120241212080256.01-83724-384-01-83953-642-X(MiAaPQ)EBC31576226(Au-PeEL)EBL31576226(CKB)36951465100041(Exl-AI)31576226(OCoLC)1478702752(EXLCZ)993695146510004120241212d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications1st ed.Stevenage :Institution of Engineering & Technology,2024.©2025.1 online resource (270 pages)Telecommunications Series1-83953-641-1 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 -- IndexGenerated by AI.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.Telecommunications Series6G mobile communication systemsGenerated by AIArtificial intelligenceGenerated by AI6G mobile communication systemsArtificial intelligence621.38456Masaracchia Antonino1824634Nguyen Khoi Khac1824635Duong Trung Q1824636Sharma Vishal851632MiAaPQMiAaPQMiAaPQBOOK9911006720403321Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications4391838UNINA