Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications
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Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications
Masaracchia, Antonino; Sharma, Vishal; Duong, Trung Q.; Nguyen, Khoi Khac
Institution of Engineering and Technology
01/2025
280
Dura
9781839536410
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Chapter 1: Artificial intelligence, machine learning and deep learning
Chapter 2: Deep neural networks
Chapter 3: Markov decision process
Chapter 4: Value function approximation for continuous state-action space
Chapter 5: Policy search methods for reinforcement learning
Chapter 6: Actor-Critic learning
Chapter 7: UAV-assisted 6G communications
Chapter 8: Distributed deep deterministic policy gradient for power allocation control in UAV-to-UAV-based communications
Chapter 9: Non-cooperative energy efficient power allocation game in UAV-to-UAV communication: A multi-agent deep reinforcement learning approach
Chapter 10: Real-time energy harvesting aided scheduling in UAV-assisted D2D networks
Chapter 11: 3D trajectory design and data collection in UAV-assisted networks
Chapter 12: RIS-assisted 6G communications
Chapter 13: Real-time optimisation in RIS-assisted D2D communications
Chapter 14: RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning
Chapter 15: Multi-agent learning in networks supported by RIS and multi-UAVs
Chapter 2: Deep neural networks
Chapter 3: Markov decision process
Chapter 4: Value function approximation for continuous state-action space
Chapter 5: Policy search methods for reinforcement learning
Chapter 6: Actor-Critic learning
Chapter 7: UAV-assisted 6G communications
Chapter 8: Distributed deep deterministic policy gradient for power allocation control in UAV-to-UAV-based communications
Chapter 9: Non-cooperative energy efficient power allocation game in UAV-to-UAV communication: A multi-agent deep reinforcement learning approach
Chapter 10: Real-time energy harvesting aided scheduling in UAV-assisted D2D networks
Chapter 11: 3D trajectory design and data collection in UAV-assisted networks
Chapter 12: RIS-assisted 6G communications
Chapter 13: Real-time optimisation in RIS-assisted D2D communications
Chapter 14: RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning
Chapter 15: Multi-agent learning in networks supported by RIS and multi-UAVs
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artificial intelligence; deep neural networks; UAV-based communication; real-time optimisation; D2D networks
Chapter 1: Artificial intelligence, machine learning and deep learning
Chapter 2: Deep neural networks
Chapter 3: Markov decision process
Chapter 4: Value function approximation for continuous state-action space
Chapter 5: Policy search methods for reinforcement learning
Chapter 6: Actor-Critic learning
Chapter 7: UAV-assisted 6G communications
Chapter 8: Distributed deep deterministic policy gradient for power allocation control in UAV-to-UAV-based communications
Chapter 9: Non-cooperative energy efficient power allocation game in UAV-to-UAV communication: A multi-agent deep reinforcement learning approach
Chapter 10: Real-time energy harvesting aided scheduling in UAV-assisted D2D networks
Chapter 11: 3D trajectory design and data collection in UAV-assisted networks
Chapter 12: RIS-assisted 6G communications
Chapter 13: Real-time optimisation in RIS-assisted D2D communications
Chapter 14: RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning
Chapter 15: Multi-agent learning in networks supported by RIS and multi-UAVs
Chapter 2: Deep neural networks
Chapter 3: Markov decision process
Chapter 4: Value function approximation for continuous state-action space
Chapter 5: Policy search methods for reinforcement learning
Chapter 6: Actor-Critic learning
Chapter 7: UAV-assisted 6G communications
Chapter 8: Distributed deep deterministic policy gradient for power allocation control in UAV-to-UAV-based communications
Chapter 9: Non-cooperative energy efficient power allocation game in UAV-to-UAV communication: A multi-agent deep reinforcement learning approach
Chapter 10: Real-time energy harvesting aided scheduling in UAV-assisted D2D networks
Chapter 11: 3D trajectory design and data collection in UAV-assisted networks
Chapter 12: RIS-assisted 6G communications
Chapter 13: Real-time optimisation in RIS-assisted D2D communications
Chapter 14: RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning
Chapter 15: Multi-agent learning in networks supported by RIS and multi-UAVs
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
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