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Deep Reinforcement Learning for RIS by Antonino Masaracchia (.PDF)

Posted Wednesday at 04:14 AM3 days

Legendary Reader

📮 Deep Reinforcement Learning for RIS by Antonino Masaracchia (.PDF)

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. The book chapters are arranged into four parts according to their topics. More specifically, the first two parts provide background and fundamentals on Artificial Intelligence and Deep Reinforcement Learning, while the last two illustrate how these principles can be applied to solve complex optimisation problems in the context of UAV-enabled and RIS-assisted networks. In Part I, there are two chapters that provide an essential introduction to Artificial Intelligence and deep neural networks. Specifically, Chapter 1 illustrates the relationship between Artificial Intelligence, Machine Learning, and Deep Learning. After introducing these concepts, Chapter 2 delves into the principles of deep reinforcement learning.