Additional information
by Deepak Gupta (Editor), Mahmoud Ragab (Editor), Romany Fouad Mansour (Editor)
AI-ENABLED 6G NETWORKS AND APPLICATIONS
Provides authoritative guidance on utilizing AI techniques in 6G network design and optimization
Written and edited by active researchers, this book covers hypotheses and practical considerations and provides insights into the design of evolutionary AI algorithms for 6G networks, with focus on network transparency, interpretability and simulatability for vehicular networks, space systems, surveillance systems and their usages in different emerging engineering fields.
AI-Enabled 6G Networks and Applications includes a review of AI techniques for 6G Networks and will focus on deployment of AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. This book includes the design of a set of evolutionary AI hybrid algorithms with communication protocols, showing how to use them in practice to solve problems relating to vehicular networks, aerial networks, and communication networks.
- Reviews various types of AI techniques such as AI-empowered mobile edge computing, intelligent handover management, and smart spectrum management
- Describes how AI techniques manage computation efficiency, algorithm robustness, hardware development, and energy management
- Identifies and provides solutions to problems in current 4G/5G networks and emergent 6G architectures
- Discusses privacy and security issues in IoT-enabled 6G Networks
- Examines the use of machine learning to achieve closed-loop optimization and intelligent wireless communication
AI-Enabled 6G Networks and Applications is an essential reference guide to advanced hybrid computational intelligence methods for 6G supportive networks and protocols, suitable for graduate students and researchers in network forensics and optimization, computer science, and engineering.
Back Jacket
Provides authoritative guidance on utilizing AI techniques in 6G network design and optimization
Written and edited by active researchers, this book covers hypotheses and practical considerations and provides insights into the design of evolutionary AI algorithms for 6G networks, with focus on network transparency, interpretability and simulatability for vehicular networks, space systems, surveillance systems and their usages in different emerging engineering fields.
AI-Enabled 6G Networks and Applications includes a review of AI techniques for 6G Networks and will focus on deployment of AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. This book includes the design of a set of evolutionary AI hybrid algorithms with communication protocols, showing how to use them in practice to solve problems relating to vehicular networks, aerial networks, and communication networks.
- Reviews various types of AI techniques such as AI-empowered mobile edge computing, intelligent handover management, and smart spectrum management
- Describes how AI techniques manage computation efficiency, algorithm robustness, hardware development, and energy management
- Identifies and provides solutions to problems in current 4G/5G networks and emergent 6G architectures
- Discusses privacy and security issues in IoT-enabled 6G Networks
- Examines the use of machine learning to achieve closed-loop optimization and intelligent wireless communication
AI-Enabled 6G Networks and Applications is an essential reference guide to advanced hybrid computational intelligence methods for 6G supportive networks and protocols, suitable for graduate students and researchers in network forensics and optimization, computer science, and engineering.
Author Biography
Deepak Gupta, Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India. His research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.
Mahmoud Ragab, PhD., Department of Information Technology, King Abdulaziz University, Saudi Arabia. Head of the Biological Quantum Computing Unit in the Centre for Artificial Intelligence in Precision Medicine. Associate Professor, Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt.
Romany Fouad Mansour, PhD., Department of Mathematics, New Valley University, Egypt. His research interests include Artificial Intelligence, Pattern Recognition, Computer Vision, Computer Networks, Soft Computing, Image Processing, Bioinformatics and Evolutionary Computation.
Aditya Khamparia, Assistant Professor, Baba Saheb Bhimrao Amedkar (Central) University, Amethi, India. His teaching and research focus on machine learning, deep learning, educational technologies, and computer vision.
Ashish Khanna, Assistant Professor, Maharaja Agrasen Institute of Technology, Delhi, India. He has extensive academic and industry experience in areas such as image processing, distributed systems, and machine learning.
