東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Reinforcement learning for reconfigurable intelligent surfaces = assisted wireless communication systems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reinforcement learning for reconfigurable intelligent surfaces/ by Alice Faisal ... [et al.].
Reminder of title:	assisted wireless communication systems /
other author:	Faisal, Alice.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	ix, 57 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Reinforcement learning. -
Online resource:	https://doi.org/10.1007/978-3-031-52554-4
ISBN:	9783031525544

Reinforcement learning for reconfigurable intelligent surfaces = assisted wireless communication systems /
Reinforcement learning for reconfigurable intelligent surfacesassisted wireless communication systems /[electronic resource] :by Alice Faisal ... [et al.]. - Cham :Springer Nature Switzerland :2024. - ix, 57 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

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 a range 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.

ISBN: 9783031525544

Standard No.: 10.1007/978-3-031-52554-4doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Reinforcement learning for reconfigurable intelligent surfaces = assisted wireless communication systems /
LDR:03735nmm a2200337 a 4500 001 2368626
003 DE-He213
005 20240223165359.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031525544 $q (electronic bk.)
020 $a 9783031525537 $q (paper)
024 7 $a 10.1007/978-3-031-52554-4 $2 doi
035 $a 978-3-031-52554-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.6
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.6 $b .R367 2024
245 0 0 $a Reinforcement learning for reconfigurable intelligent surfaces $h [electronic resource] : $b assisted wireless communication systems / $c by Alice Faisal ... [et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a ix, 57 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5776
505 0 $a 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.
520 $a 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 a range 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.
650 0 $a Reinforcement learning. $3 1006373
650 0 $a Wireless communication systems. $3 567106
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Wireless and Mobile Communication. $3 3338159
650 2 4 $a Computer Communication Networks. $3 775497
700 1 $a Faisal, Alice. $3 3712652
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computer science. $3 1567571
856 4 0 $u https://doi.org/10.1007/978-3-031-52554-4
950 $a Computer Science (SpringerNature-11645)