Trung Q. Duong

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.

There is no doubt that we are facing a wireless data explosion. Modern wireless networks need to satisfy increasing demand, but are faced with challenges such as limited spectrum, expensive resources, green communication requirements and security issues. In the age of internet of things (IoT) with massive data transfers and huge numbers of connected devices, including high-demand QoS (4G, 5G networks and beyond), signal processing is producing data sets at the gigabyte and terabyte scales. Modest-sized optimisation problems can be handled by online algorithms with fast speed processing and a huge amount of computer memory. With the rapid increase in powerful computers, more efficient algorithms and advanced parallel computing promise an enormous reduction in calculation time, solving modern optimisation problems on strict deadlines at microsecond or millisecond time scales. Finally, the interplay between machine learning and optimisation is an efficient and practical approach to optimisation in real-time applications. Real-time optimisation is becoming a reality in signal processing and wireless networks. This book considers advanced real-time optimisation methods for 5G and beyond networks. The authors discuss the fundamentals, technologies, practical questions and challenges around real-time optimisation of 5G and beyond communications, providing insights into relevant theories, models and techniques. The book should benefit a wide audience of researchers, practitioners, scientists, professors and advanced students in engineering, computer science, ubiquitous computing, information technology, and networking and communications engineering, as well as professionals in government agencies.