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A01=Antonino Masaracchia
A01=Khoi Khac Nguyen
A01=Trung Q. Duong
A01=Vishal Sharma
Age Group_Uncategorized
Age Group_Uncategorized
Author_Antonino Masaracchia
Author_Khoi Khac Nguyen
Author_Trung Q. Duong
Author_Vishal Sharma
automatic-update
Category1=Non-Fiction
Category=TGPC
Category=TRP
Category=UB
Category=UT
Category=UYQM
COP=United Kingdom
Delivery_Pre-order
Language_English
PA=Not yet available
Price_€100 and above
PS=Forthcoming
softlaunch

Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications

Reconfigurable intelligent surface (RIS) has emerged as a cutting-edge technology for beyond 5G and 6G networks due to its low-cost hardware production, nearly passive nature, easy deployment, communication without new waves, and energy-saving benefits. Unmanned aerial vehicle (UAV)-assisted wireless networks significantly enhance network coverage.

Resource allocation and real-time decision-making optimisation play a pivotal role in approaching the optimal performance in UAV- and RIS-aided wireless communications. But the existing contributions typically assume having a static environment and often ignore the stringent flight time constraints in real-life applications. It is crucial to improve the decision-making time for meeting the stringent requirements of UAV-assisted wireless networks. Deep reinforcement learning (DRL), which is a combination of reinforcement learning and neural networks, is used to maximise network performance, reduce power consumption, and improve the processing time for real-time. DRL algorithms can help UAVs and RIS work fully autonomously, reduce energy consumption and operate optimally in an unexpected environment.

This co-authored book explores the many challenges arising from real-time and autonomous decision-making for 6G. The goal is to provide readers with comprehensive insights into the models and techniques of deep reinforcement learning and its applications in 6G networks and internet-of-things with the support of UAVs and RIS.

Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications is aimed at a wide audience of researchers, practitioners, scientists, professors and advanced students in engineering, computer science, information technology, and communication engineering, and networking and ubiquitous computing professionals.

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Current price €117.79
Original price €123.99
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A01=Antonino MasaracchiaA01=Khoi Khac NguyenA01=Trung Q. DuongA01=Vishal SharmaAge Group_UncategorizedAuthor_Antonino MasaracchiaAuthor_Khoi Khac NguyenAuthor_Trung Q. DuongAuthor_Vishal Sharmaautomatic-updateCategory1=Non-FictionCategory=TGPCCategory=TRPCategory=UBCategory=UTCategory=UYQMCOP=United KingdomDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€100 and abovePS=Forthcomingsoftlaunch

Will deliver when available. Publication date 01 Dec 2024

Product Details
  • Dimensions: 156 x 234mm
  • Publication Date: 01 Dec 2024
  • Publisher: Institution of Engineering and Technology
  • Publication City/Country: United Kingdom
  • Language: English
  • ISBN13: 9781839536410

About Antonino MasaracchiaKhoi Khac NguyenTrung Q. DuongVishal Sharma

Antonino Masaracchia is a lecturer at Queen Mary University of London UK. He has contributed as a guest editor to journal special issues including IET Communications. He is involved in international conferences as a publicity chair social media chair TPC member tutorial chair and reviewer. His research interests include digital twins URLLCs UAV-communications real-time optimization and AI and ML techniques for wireless communication networks. Khoi Khac Nguyen completed his PhD degree with the School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK. His research interests include machine learning and deep reinforcement learning for real-time optimisation in wireless networks reconfigurable intelligent surfaces unmanned air vehicle (UAV) communication and massive internet of things (IoTs). Trung Q. Duong is a Canada Excellence Research Chair (CERC) and full professor at Memorial University Canada. He is also a chair professor in telecommunications at Queen's University Belfast UK and a research chair of the Royal Academy of Engineering. His research interests include machine learning real-time optimisation data analytics and 5G-6G networks. He is a recipient of the Royal Academy of Engineering Research Fellowship (2016-2020) and won the Newton Prize in 2017. Vishal Sharma is a senior lecturer in the School of Electronics Electrical Engineering and Computer Science (EEECS) at Queen's University Belfast (QUB) Northern Ireland UK. His research focuses on autonomous systems UAV communications network behaviour modelling 5G and beyond blockchain and CPS security. He has authored and co-authored over 100 journal and conference articles and book chapters and co-edited two books. He is a senior member of IEEE and a professional member of ACM.

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