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

• Main Authors: Masaracchia, Antonino, Nguyen, Khoi Khac, Duong, Trung Q., Sharma, Vishal
• Format: eBook
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 2025; Institution of Engineering and Technology (The IET); Institution of Engineering & Technology; Institution of Engineering and Technology
• Edition: 1
• Series: Telecommunications
• Subjects: 6G mobile communication systems; Aerospace & Radar Technology; General References; Reinforcement learning; Signal processing; Software Engineering; TECHNOLOGY & ENGINEERING; Telecommunications; UAV-based communication; D2D networks; artificial intelligence; deep neural networks; real-time optimisation
• ISBN: 1839536411; 9781839536410
• DOI: 10.1049/PBTE106E

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 applications. 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.