9789999330060 - Artificial Intelligence for Communication Engineering: The Intricacies of Deep Learning Applications in Wireless Systems de Shrivastava, Shivanshu (10 resultados)

Paperback. Condición: Brand New. 187 pages. 6.00x0.43x9.00 inches. In Stock.

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Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: Neu. Artificial Intelligence for Communication Engineering | The Intricacies of Deep Learning Applications in Wireless Systems | Shivanshu Shrivastava | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999330060 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence for Communication Engineering offers a groundbreaking exploration into how deep learning (DL) and deep reinforcement learning (DRL) can transform the performance of modern wireless communication systems. With the increasing demand for intelligent, adaptive, and energy-efficient communication networks, this book serves as a timely resource for researchers, engineers, and graduate students alike.Beginning with hybrid systems that integrate radio frequency (RF) and visible light communication (VLC), the book proposes innovative DL frameworks for optimizing bandwidth, power, and user allocation. It introduces deep Q-network (DQN) algorithms to maximize system throughput-demonstrating a significant gain compared to conventional optimization-based models. The discussion then evolves to dynamic indoor scenarios such as hospitals and airports, where transfer learning-based DQN models adapt to user mobility, drastically reducing training time while enhancing data throughput.The book further presents a two-stage deep transfer learning (DTL) strategy for high-complexity hybrid RF/free space optical systems, achieving up to 80% improvement in learning speed. It explores model-free DRL techniques for hybrid RF/LiFi networks, accounting for real-world factors like mobility and signal blockage, with results showing over 60% improvement in data rates.Later chapters focus on on-policy DRL strategies such as advantage actor-critic and proximal policy optimization, which outperform traditional DRL algorithms in large-scale heterogeneous networks. The book also dives into DL applications for massive MIMO systems using discrete Rajan transform for CSI feedback reduction, showcasing 120% improvement in estimation accuracy. Finally, it addresses cognitive radio systems under security threats, proposing DQN-based defense mechanisms that outperform legacy systems.Each chapter is supported by rigorous simulations in highly practical conditions. It shows the transformative power of AI in next-generation networks. More than a technical guide, this book is a vision for the intelligent, adaptive, and secure wireless communication systems of tomorrow.

Hardcover. Condición: new. Hardcover. Artificial Intelligence for Communication Engineering offers a groundbreaking exploration into how deep learning (DL) and deep reinforcement learning (DRL) can transform the performance of modern wireless communication systems. With the increasing demand for intelligent, adaptive, and energy-efficient communication networks, this book serves as a timely resource for researchers, engineers, and graduate students alike. Beginning with hybrid systems that integrate radio frequency (RF) and visible light communication (VLC), the book proposes innovative DL frameworks for optimizing bandwidth, power, and user allocation. It introduces deep Q-network (DQN) algorithms to maximize system throughput-demonstrating a significant gain compared to conventional optimization-based models. The discussion then evolves to dynamic indoor scenarios such as hospitals and airports, where transfer learning-based DQN models adapt to user mobility, drastically reducing training time while enhancing data throughput. The book further presents a two-stage deep transfer learning (DTL) strategy for high-complexity hybrid RF/free space optical systems, achieving up to 80% improvement in learning speed. It explores model-free DRL techniques for hybrid RF/LiFi networks, accounting for real-world factors like mobility and signal blockage, with results showing over 60% improvement in data rates. Later chapters focus on on-policy DRL strategies such as advantage actor-critic and proximal policy optimization, which outperform traditional DRL algorithms in large-scale heterogeneous networks. The book also dives into DL applications for massive MIMO systems using discrete Rajan transform for CSI feedback reduction, showcasing 120% improvement in estimation accuracy. Finally, it addresses cognitive radio systems under security threats, proposing DQN-based defense mechanisms that outperform legacy systems. Each chapter is supported by rigorous simulations in highly practical conditions. It shows the transformative power of AI in next-generation networks. More than a technical guide, this book is a vision for the intelligent, adaptive, and secure wireless communication systems of tomorrow. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: new. Hardcover. Artificial Intelligence for Communication Engineering offers a groundbreaking exploration into how deep learning (DL) and deep reinforcement learning (DRL) can transform the performance of modern wireless communication systems. With the increasing demand for intelligent, adaptive, and energy-efficient communication networks, this book serves as a timely resource for researchers, engineers, and graduate students alike. Beginning with hybrid systems that integrate radio frequency (RF) and visible light communication (VLC), the book proposes innovative DL frameworks for optimizing bandwidth, power, and user allocation. It introduces deep Q-network (DQN) algorithms to maximize system throughput-demonstrating a significant gain compared to conventional optimization-based models. The discussion then evolves to dynamic indoor scenarios such as hospitals and airports, where transfer learning-based DQN models adapt to user mobility, drastically reducing training time while enhancing data throughput. The book further presents a two-stage deep transfer learning (DTL) strategy for high-complexity hybrid RF/free space optical systems, achieving up to 80% improvement in learning speed. It explores model-free DRL techniques for hybrid RF/LiFi networks, accounting for real-world factors like mobility and signal blockage, with results showing over 60% improvement in data rates. Later chapters focus on on-policy DRL strategies such as advantage actor-critic and proximal policy optimization, which outperform traditional DRL algorithms in large-scale heterogeneous networks. The book also dives into DL applications for massive MIMO systems using discrete Rajan transform for CSI feedback reduction, showcasing 120% improvement in estimation accuracy. Finally, it addresses cognitive radio systems under security threats, proposing DQN-based defense mechanisms that outperform legacy systems. Each chapter is supported by rigorous simulations in highly practical conditions. It shows the transformative power of AI in next-generation networks. More than a technical guide, this book is a vision for the intelligent, adaptive, and secure wireless communication systems of tomorrow. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.