LEADER 00851nam0-22002771i-450- 001 990001304760403321 035 $a000130476 035 $aFED01000130476 035 $a(Aleph)000130476FED01 035 $a000130476 100 $a20000920d--------km-y0itay50------ba 101 0 $aeng 200 1 $aBiomathematics the principles of mathematics for students of biological and general science$fby SMITH C.A.B. 610 0 $aBiomatematica$aManuali 700 1$aSmith,$bCedric A.B.$059173 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001304760403321 952 $a7-B-6-(1$b16646$fMA1 952 $a7-B-6-(2$b16647$fMA1 959 $aMA1 962 $a92-01 962 $a00A05 996 $aBiomathematics the principles of mathematics for students of biological and general science$9383252 997 $aUNINA DB $aING01 LEADER 03391nam 22005293 450 001 9911006720403321 005 20241212080256.0 010 $a1-83724-384-0 010 $a1-83953-642-X 035 $a(MiAaPQ)EBC31576226 035 $a(Au-PeEL)EBL31576226 035 $a(CKB)36951465100041 035 $a(Exl-AI)31576226 035 $a(OCoLC)1478702752 035 $a(EXLCZ)9936951465100041 100 $a20241212d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications 205 $a1st ed. 210 1$aStevenage :$cInstitution of Engineering & Technology,$d2024. 210 4$dİ2025. 215 $a1 online resource (270 pages) 225 1 $aTelecommunications Series 311 08$a1-83953-641-1 327 $aContents -- 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$7Generated by AI. 330 $aThis co-authored book explores the many challenges arising from real-time and autonomous decision-making for 6G by covering crucial advanced signal control and real-time decision-making methods for UAV- and RIS-assisted 6G wireless communications including the serious constraints in real-time optimisation problems. 410 0$aTelecommunications Series 606 $a6G mobile communication systems$7Generated by AI 606 $aArtificial intelligence$7Generated by AI 615 0$a6G mobile communication systems 615 0$aArtificial intelligence 676 $a621.38456 700 $aMasaracchia$b Antonino$01824634 701 $aNguyen$b Khoi Khac$01824635 701 $aDuong$b Trung Q$01824636 701 $aSharma$b Vishal$0851632 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911006720403321 996 $aDeep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications$94391838 997 $aUNINA