<?xml version="1.0" encoding="utf-8"?><rss version="2.0"><channel><title>Antonino Masaracchia, Khoi Khac Nguyen, Trung Q. Duong, Vishal Sharma</title><link>https://shop.theiet.org:443/author/antonino-masaracchia-khoi-khac-nguyen-trung-q-duong-vishal-sharma</link><description>Antonino Masaracchia, Khoi Khac Nguyen, Trung Q. Duong, Vishal Sharma</description><item><title>Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications</title><link>https://shop.theiet.org:443/deep-reinforcement-learning-for-reconfigurable-intelligent-surfaces-and-uav-empowered-smart-6g-communications</link><description>&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;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.&lt;/p&gt;
&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;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.&lt;/p&gt;
&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;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.&lt;/p&gt;
&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;&lt;i&gt;Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications&lt;/i&gt; 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.&lt;/p&gt;</description><pubDate>Mon, 16 Sep 2024 09:17:53 GMT</pubDate><guid isPermaLink="true">https://shop.theiet.org:443/deep-reinforcement-learning-for-reconfigurable-intelligent-surfaces-and-uav-empowered-smart-6g-communications</guid></item></channel></rss>