Reinforcement Learning for Reconfigurable Intelligent Surfaces [electronic resource] : Assisted Wireless Communication Systems / by Alice Faisal, Ibrahim Al-Nahhal, Octavia A. Dobre, Telex M. N. Ngatched.

Por: Faisal, Alice [author.]Colaborador(es): Al-Nahhal, Ibrahim [author.] | Dobre, Octavia A [author.] | Ngatched, Telex M. N [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries SpringerBriefs in Computer ScienceEditor: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: IX, 57 p. 12 illus., 11 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031525544Tema(s): Machine learning | Wireless communication systems | Mobile communication systems | Computer networks  | Machine Learning | Wireless and Mobile Communication | Computer Communication NetworksFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.31 Clasificación LoC:Q325.5-.7Recursos en línea: Libro electrónicoTexto
Contenidos:
Preface -- Chapter. 1. Reinforcement Learning Background -- Chapter. 2. RIS-AssistedWireless Systems -- Chapter. 3. Applications of RL for Continuous Problems in RIS-Assisted Communication Systems -- Chapter. 4. Applications of RL for Discrete Problems in RIS-Assisted Communication Systems -- Chapter. 5. Challenges and Future Work -- .
En: Springer Nature eBookResumen: This book presents the intersection of two dynamic fields: Reinforcement Learning (RL) and RIS- Assisted Wireless Communications. With an emphasis on both discrete and continuous problems, it introduces a comprehensive overview of RL techniques and their applications in the evolving world of RIS-assisted wireless communications. Chapter 1 introduces the fundamentals of RL and deep RL (DRL), providing a solid foundation for understanding subsequent chapters. It also presents the Q-learning, deep Q-learning, and deep deterministic policy gradient algorithms. Chapter 2 provides a holistic overview of RIS-assisted systems and details several use cases in wireless communications. Then, Chapters 3 and 4 present various applications of the discrete and continuous DRL to RIS-assisted wireless communications. From maximizing the sum-rate to minimizing, the system resources and maximizing the energy efficiency. These chapters showcase the versatility of the DRL algorithms in tackling arange of challenges. This book concludes with Chapter 5, which introduces the challenges and future directions in this field. It explores the particulars of hyperparameter tuning, problem design, and complexity analysis, while also highlighting the potential of hybrid DRL, multi-agent DRL, and transfer learning techniques for advancing wireless communication systems. Optimizing RIS-Assisted Wireless Systems requires powerful algorithms to cope with the dynamic propagation environment. DRL is envisioned as one of the key enabling techniques to exploit the full potential of RIS-Assisted Wireless Communication Systems. It empowers these systems to intelligently adapt to dynamic wireless environments, maximize performance metrics, and adjusts their configurations to accommodate diverse use cases efficiently. This book serves as a valuable resource, shedding light on the potential of DRL to optimize RIS-Assisted Wireless Communication, enabling researchers, engineers, advanced level students in computer science and electrical engineering and enthusiasts to grasp the intricacies of this topic. It offers a comprehensive understanding of the principles, applications, and challenges, making it a reference to recognize the full potential of the RIS technology in modern wireless communication systems.
Star ratings
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos 1 No para préstamo

Preface -- Chapter. 1. Reinforcement Learning Background -- Chapter. 2. RIS-AssistedWireless Systems -- Chapter. 3. Applications of RL for Continuous Problems in RIS-Assisted Communication Systems -- Chapter. 4. Applications of RL for Discrete Problems in RIS-Assisted Communication Systems -- Chapter. 5. Challenges and Future Work -- .

This book presents the intersection of two dynamic fields: Reinforcement Learning (RL) and RIS- Assisted Wireless Communications. With an emphasis on both discrete and continuous problems, it introduces a comprehensive overview of RL techniques and their applications in the evolving world of RIS-assisted wireless communications. Chapter 1 introduces the fundamentals of RL and deep RL (DRL), providing a solid foundation for understanding subsequent chapters. It also presents the Q-learning, deep Q-learning, and deep deterministic policy gradient algorithms. Chapter 2 provides a holistic overview of RIS-assisted systems and details several use cases in wireless communications. Then, Chapters 3 and 4 present various applications of the discrete and continuous DRL to RIS-assisted wireless communications. From maximizing the sum-rate to minimizing, the system resources and maximizing the energy efficiency. These chapters showcase the versatility of the DRL algorithms in tackling arange of challenges. This book concludes with Chapter 5, which introduces the challenges and future directions in this field. It explores the particulars of hyperparameter tuning, problem design, and complexity analysis, while also highlighting the potential of hybrid DRL, multi-agent DRL, and transfer learning techniques for advancing wireless communication systems. Optimizing RIS-Assisted Wireless Systems requires powerful algorithms to cope with the dynamic propagation environment. DRL is envisioned as one of the key enabling techniques to exploit the full potential of RIS-Assisted Wireless Communication Systems. It empowers these systems to intelligently adapt to dynamic wireless environments, maximize performance metrics, and adjusts their configurations to accommodate diverse use cases efficiently. This book serves as a valuable resource, shedding light on the potential of DRL to optimize RIS-Assisted Wireless Communication, enabling researchers, engineers, advanced level students in computer science and electrical engineering and enthusiasts to grasp the intricacies of this topic. It offers a comprehensive understanding of the principles, applications, and challenges, making it a reference to recognize the full potential of the RIS technology in modern wireless communication systems.

UABC ; Perpetuidad

Con tecnología Koha