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.
Table of Contents
Notes on Contributors
Preface
About the Editors
Chapter 1
Metaheuristic Moth Flame Optimization Based Energy Efficient Clustering Protocol for 6G Enabled Unmanned Aerial Vehicle Networks
Adnen El Amraoui
Associate Professor, Univ. Artois, U.R. 3926
Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A), Béthune, France
Chapter 2
A Novel Data Offloading with Deep Learning Enabled Cyberattack Detection Model for Edge Computing in 6G Networks
Elsaid M. Abdelrahim
Department of Mathematics Computer Science Division, Faculty of Science, Tanta University, Tanta, Egypt.
Chapter 3
Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks
1,2José Escorcia-Gutierrez, 3Melitsa Torres-Torres, 4Kelvin Beleño, 5Carlos Soto
1Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia
2Research Center - CIENS, Escuela Naval de Suboficiales A.R.C. "Barranquilla", Barranquilla, Colombia
3Research group IET-UAC,Universidad Autónoma del Caribe, 08001 Barranquilla, Colombia
4Mechatronics Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia
5Mechanical Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia
Chapter 4
Crow Search Algorithm based Vector Quantization Approach for Image Compression in 6G Enabled Industrial Internet of Things Environment
Maha M. Althobaiti
Department of Computer Science, College of Computing and Information technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Chapter 5
Design of Artificial Intelligence Enabled Dingo Optimizer for Energy Management in 6G Communication Networks
1,2Pooja Singh, 3,4Marcello Carvalho dos Reis, 5Victor Hugo C. de Albuquerque
1Postdoctoral Fellow, Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza, Ceara, Brazil.
2Associate Professor, Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Knowledge Park-3, Greater Noida (U.P.) India- 201306
3Graduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil.
4Meteora, Fortaleza/CE, Brazil
5Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza/CE, Brazil
Chapter 6
Adaptive Whale Optimization with Deep Learning Enabled RefineDet Network for Vision Assistance on 6G Networks
1,2Vinita Malik, 3,4Marcello Carvalho dos Reis, 5Victor Hugo C. de Albuquerque
1Postdoctoral Fellow, Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza, Ceara, Brazil.
2Information Scientist, Central University of Haryana, Haryana, India- 201306
3Graduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil.
4Meteora, Fortaleza/CE, Brazil
5Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza/CE, Brazil
Chapter 7
Efficient Deer Hunting Optimization Algorithm based Spectrum Sensing Approach for 6G Communication Networks
R. Pandi Selvam, Kanagaraj Narayanasamy, M. Ilayaraja
PG Department of Computer Science, Vidhyaa Giri College of Arts & Science, Karaikudi- 630 108, India
Department of Computer Applications, J.J. College of Arts and Science (Autonomous), Pudukkottai- 622 422, India
School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, India
Chapter 8
Elite Oppositional Hunger Games Search Optimization based Cooperative Spectrum Sensing Scheme for 6G Cognitive Radio Networks
Emad A-B Abdel-Salam, Ayman M. Mahmoud, Romany F. Mansour
Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
