9781032998152 - Deep Learning Approaches in Intelligent Wireless Networking (Computational Methods for Industrial Applications) (16 resultados)

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Condición: New. Bharat Bhushan is an Associate Professor at Department of Computer Science and Engineering, Sharda School of Computing Science &amp Engineering, Sharda University, Greater Noida, India.Mohd Anas Wajid is an Assistant Professor and P.

Hardcover. Condición: Brand New. 336 pages. 9.18x6.12x9.45 inches. In Stock.

Buch. Condición: Neu. Neuware - This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems, which are important in intelligent wireless communications.Features:¿ Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications.¿ Presents high-speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks.¿ Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks.¿ Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning.¿ Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection and sparse channel estimation.The book is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

Hardcover. Condición: new. Hardcover. This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems, which are important in intelligent wireless communications.Features: Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications. Presents high-speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks. Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks. Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning. Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection and sparse channel estimation.The book is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology. This book covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and elaborates discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. 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. This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems, which are important in intelligent wireless communications.Features: Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications. Presents high-speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks. Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks. Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning. Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection and sparse channel estimation.The book is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology. This book covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and elaborates discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems, which are important in intelligent wireless communications.Features: Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications. Presents high-speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks. Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks. Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning. Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection and sparse channel estimation.The book is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology. This book covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and elaborates discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.