05034nam a22005775i 4500 978-3-031-52554-4 DE-He213 20251030111440.0 cr nn 008mamaa 240223s2024 sz | s |||| 0|eng d 9783031525544 978-3-031-52554-4 10.1007/978-3-031-52554-4 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Faisal, Alice. author. aut http://id.loc.gov/vocabulary/relators/aut 71632 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. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. IX, 57 p. 12 illus., 11 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computer Science, 2191-5776 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. Machine learning. 55059 Wireless communication systems. 65781 Mobile communication systems. 65782 Computer networks . 60157 Machine Learning. 55059 Wireless and Mobile Communication. 65786 Computer Communication Networks. 65689 Al-Nahhal, Ibrahim. author. aut http://id.loc.gov/vocabulary/relators/aut 71633 Dobre, Octavia A. author. aut http://id.loc.gov/vocabulary/relators/aut 71634 Ngatched, Telex M. N. author. aut http://id.loc.gov/vocabulary/relators/aut 71635 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031525537 Printed edition: 9783031525551 SpringerBriefs in Computer Science, 2191-5776 66578 https://doi.org/10.1007/978-3-031-52554-4 ZDB-2-SCS ZDB-2-SXCS 57835 57835