AI-Enabled 6G Networks and Applications

Description

AI-ENABLED 6G NETWORKS AND APPLICATIONSProvides 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. Cover Title Page Copyright Page Contents List of Contributors Preface About the Editors Chapter 1 Metaheuristic Moth Flame Optimization Based Energy Efficient Clustering Protocol for 6G Enabled Unmanned Aerial Vehicle Networks 1.1 Introduction 1.2 The Proposed Model 1.2.1 Network Model 1.2.2 Algorithmic Procedure of MFO Algorithm 1.2.3 Design of MMFO-EEC Technique 1.3 Experimental Validation 1.4 Conclusion References Chapter 2 A Novel Data Offloading with Deep Learning Enabled Cyberattack Detection Model for Edge Computing in 6G Networks 2.1 Introduction 2.2 The Proposed Model 2.2.1 RNN Based Traffic Flow Forecasting 2.2.2 ASCE Based Data Offloading 2.2.3 SAE Based Cyberattack Detection 2.2.4 CSO Based Parameter Optimization 2.3 Performance Validation 2.4 Conclusion References Chapter 3 Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks 3.1 Introduction 3.2 The Proposed Model 3.2.1 Z-Score Normalization 3.2.2 DBN Based Prediction Model 3.2.3 HSGO Based Hyperparameter Optimization Model 3.3 Experimental Validation 3.4 Conclusion References Chapter 4 Crow Search Algorithm Based Vector Quantization Approach for Image Compression in 6G Enabled Industrial Internet of Things Environment 4.1 Introduction 4.2 The Proposed Model 4.2.1 Overview of VQ 4.2.2 LBG Model 4.2.3 Process Involved in CSAVQ-ICIIoT Model 4.3 Results and Discussion 4.4 Conclusion References Chapter 5 Design of Artificial Intelligence Enabled Dingo Optimizer for Energy Management in 6G Communication Networks 5.1 Introduction 5.2 The Proposed Model 5.2.1 Process Involved in DOA 5.2.2 Steps Involved in Energy Management Scheme 5.3 Experimental Validation 5.4 Conclusion References Chapter 6 Adaptive Whale Optimization with Deep Learning Enabled RefineDet Network for Vision Assistance on 6G Networks 6.1 Introduction 6.2 The Proposed Model 6.2.1 Image Augmentation and Annotation 6.2.2 RefineDet Based Object Detection 6.2.3 Hyperparameter Tuning Using AWO Algorithm 6.2.4 Distance Measurement 6.3 Results and Discussion 6.4 Conclusion References Chapter 7 Efficient Deer Hunting Optimization Algorithm Based Spectrum Sensing Approach for 6G Communication Networks 7.1 Introduction 7.2 Related Works 7.3 The Proposed Model 7.4 Experimental Validation 7.5 Conclusion References Chapter 8 Elite Oppositional Hunger Games Search Optimization Based Cooperative Spectrum Sensing Scheme for 6G Cognitive Radio Networks 8.1 Introduction 8.2 Related Works 8.3 The Proposed Model 8.3.1 Design of EOHGSO Algorithm 8.3.2 Application of EOHGSO Algorithm for CSS 8.4 Experimental Validation 8.5 Conclusion References Index EULA