Preface xxi Acknowledgements xxiii 1 Introduction to AI Techniques for 6G Application 1 Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani and Shippu Sachdeva 1.1 Introduction 2 1.2 Different Generation of Communication: From 1G to 6G 4 1.2.1 First Generation (1G) 4 1.2.2 Second Generation (2G) 5 1.2.
3 Third Generation (3G) 5 1.2.4 Fourth Generation (4G) 5 1.2.5 Fifth Generation (5G) 5 1.2.6 Sixth Generation (6G) 5 1.3 Key Features and Requirements of 6G Networks 6 1.
3.1 Faster Data Speeds 6 1.3.2 Ultra-Low Latency 6 1.3.3 Massive Capacity 7 1.3.4 Energy Efficiency 7 1.
3.5 Seamless Connectivity 7 1.3.6 Advanced Spectrum Management 7 1.3.7 Enhanced Security and Privacy 7 1.3.8 Artificial Intelligence Integration 7 1.
3.9 Heterogeneous Network Architecture 8 1.4 Role of Artificial Intelligence in 6G 8 1.4.1 Intelligent Radio Resource Management 9 1.4.2 Beamforming and MIMO 9 1.4.
3 Intelligent Network Slicing 9 1.4.4 Intelligent Edge Computing 9 1.4.5 Intelligent Internet of Things 9 1.4.6 Enhanced Privacy 10 1.4.
7 Intelligent Network Organization 10 1.4.8 Intelligent User Experience and Services 10 1.5 Machine Learning for 6G Networks 10 1.5.1 Intelligent Resource Management 11 1.5.2 Dynamic Spectrum Access 11 1.
5.3 Intelligent Beamforming 11 1.5.4 Network Anomaly Detection 11 1.5.5 Intelligent Edge Computing 11 1.5.6 Intelligent Internet of Things 12 1.
5.7 Intelligent Network Slicing 12 1.5.8 Intelligent Network Planning and Optimization 12 1.5.9 Predictive Maintenance 12 1.6 Deep Learning for 6G Applications 12 1.6.
1 Enhanced Communication Systems 13 1.6.2 Intelligent Beamforming and Antenna Systems 13 1.6.3 Image and Video Processing 13 1.6.4 Intelligent Internet of Things 13 1.6.
5 Autonomous Systems 13 1.6.6 Natural Language Processing and Speech Recognition 14 1.6.7 Augmented Reality and Virtual Reality 14 1.6.8 Network Security 14 1.7 Edge Computing and AI in 6G 14 1.
7.1 Distributed Intelligence 14 1.7.2 Low-Latency Applications 15 1.7.3 Intelligent Edge Devices 15 1.7.4 Edge-AI-Assisted Network Management 15 1.
7.5 Federated Learning 15 1.7.6 AI-Driven Security 15 1.7.7 Edge-AI for Content Delivery 16 1.7.8 Context-Aware Applications 16 1.
8 AI-Enhanced Network Security in 6G 16 1.8.1 Threat Detection and Prevention 16 1.8.2 Anomaly Detection 17 1.8.3 Intrusion Detection and Prevention Systems (IDPS) 17 1.8.
4 User Authentication 17 1.8.5 AI-Enabled Threat Intelligence 17 1.8.6 Automated Security Incident Response 17 1.8.7 AI-Enhanced Security Analytics 18 1.8.
8 Privacy-Preserving Techniques 18 1.9 Quantum Computing and AI Fusion in 6G 18 1.9.1 Enhanced AI Algorithms 18 1.9.2 Optimization and Search Problems 19 1.9.3 Security and Encryption 19 1.
9.4 Quantum-Assisted Machine Learning 19 1.9.5 Quantum Sensor Networks 19 1.9.6 Quantum-Assisted Simulation 19 1.9.7 Quantum Machine Learning 20 1.
9.8 Quantum-Assisted Optimization 20 1.10 AI for Smart City Applications in 6G 20 1.10.1 Intelligent Traffic Management 20 1.10.2 Energy Management and Sustainability 21 1.10.
3 Smart Infrastructure Monitoring 21 1.10.4 Waste Management 21 1.10.5 Smart Public Security and Safety 21 1.10.6 AI-Enabled Citizen Services 21 1.10.
7 Urban Planning and Design 22 1.10.8 Data Analytics and Insights 22 1.11 Challenges and Future Directions 22 1.11.1 Technical Complexity 22 1.11.1.
1 Future Directions 23 1.11.2 Privacy and Security 23 1.11.2.1 Future Directions 23 1.11.3 Ethical Considerations 24 1.
11.3.1 Future Directions 24 1.11.4 Infrastructure and Energy Efficiency 24 1.11.4.1 Future Directions 24 1.
11.5 Collaboration and Standardization 24 1.11.5.1 Future Directions 25 1.11.6 Socioeconomic Impact 25 1.11.
6.1 Future Directions 25 1.11.7 Environmental Sustainability 25 1.11.7.1 Future Directions 25 1.12 Conclusion 25 References 26 2 AI Techniques for 6G Applications 29 Jyoti R.
Munavalli, Rashmi R. Deshpande and Jayashree M. Oli 2.1 6G Communication 30 2.2 Artificial Intelligence (AI) Computing in 6G 34 2.3 Role of AI in 6G 37 2.4 AI Techniques for 6G 38 2.4.
1 Supervised Learning 39 2.4.2 Unsupervised Learning 41 2.4.3 Reinforcement Learning 42 2.4.4 Federated Learning 44 2.4.
5 Deep Learning 46 2.5 Use Cases/Applications 47 2.5.1 Holographic Applications 47 2.5.2 Ubiquitous Computing 48 2.5.3 Deep Sensing/Tactile Internet 50 2.
5.4 Dynamic Spectrum Allocation 51 2.6 Conclusion 53 References 53 3 An Evaluation of Pervasive Computing Using Narrowband Technology: Exploring the Implications for 5G and Future Generations 57 Sriharipriya K. C., Athira Soman Nair, Kannanpuzha Chelsea Antony, Megha Nair B. and Amala Ipe 3.1 Introduction 58 3.2 Features 59 3.
2.1 Power Consumption 59 3.2.2 Improved Coverage and Sensitivity with Low Latency 61 3.2.3 Transmission Mode 61 3.2.4 Resource of Spectrum 62 3.
2.5 Mode of Working 62 3.2.6 Structure of Frame 64 3.2.7 Network of NB-IoT 64 3.2.8 Semi-Static Link Adaptation 66 3.
2.9 Retransmission of Data 66 3.3 Basic Principles and Core Technologies of Narrowband 67 3.3.1 Theory of Analysis of Connection 67 3.3.2 Theory of Latency Survey 68 3.3.
3 The Mechanism for Coverage Enhancement 69 3.3.4 Technology with Ultra-Low Power 70 3.3.5 Relationship of Coupling Between Signaling and Data 71 3.4 Correlation of Other Communication Technology with NB-IoT 72 3.4.1 With eMTC Technology 72 3.
4.1.1 Coverage 74 3.4.1.2 Power Consumption 75 3.4.1.
3 Connection Count 75 3.4.1.4 Voice Assistance 76 3.4.1.5 Mobility Management 76 3.4.
1.6 Network Deployment''s Effect on the Current Network 76 3.4.1.7 Operative Mode 77 3.4.1.8 Combined Results 77 3.
4.2 With More Wireless Network Methods 77 3.5 Applications 80 3.6 Security Needs 83 3.6.1 Perception Layer 84 3.6.2 Transmission Layers 85 3.
6.3 Application Layer 86 3.7 Conclusion 87 References 88 4 Cumulant-Based Performance Analysis of 5G and 6G Communication Networks 93 Madhusmita Mishra, Sarat Kumar Patra and Ashok Kumar Turuk 4.1 Introduction 94 4.2 Performance Analysis of the Modified BSLM Technique Using PAPR Characteristics and Various Phase Sequences 96 4.2.1 Overview of SLM-Based PAPR Reduction and Modification 96 4.2.
2 PAPR Reduction Analysis Using CCDF 100 4.2.3 Analysis of PAPR Reduction Using Various Phase Sequences 101 4.3 Mutual Independency Basing on Joint Cumulants 108 4.4 Computational Complexity 110 4.5 Conclusion 110 References 111 5 Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing 115 G. Vinuja and N. Bharatha Devi 5.
1 Introduction 116 5.2 Literature Review 118 5.2.1 Overview of 6G Networks and Their Potential Benefits in Disaster Management 127 5.3 Real-Time Disaster Monitoring and Management Using Remote Technologies 128 5.3.1 Enhanced Connectivity 128 5.3.
2 Remote Sensing and Monitoring 128 5.3.3 Data Analytics and AI 129 5.3.4 Virtual Reality (VR) and Augmented Reality (AR) 129 5.3.5 Telemedicine and Remote Healthcare 129 5.3.
6 Public Awareness and Communication 129 5.3.7 Smart Infrastructure and IoT Integration 130 5.3.8 Quicker Response Times 130 5.3.9 Enhanced Risk Assessment 130 5.3.
10 Resource Allocation Optimization 130 5.3.11 Enhanced Coordination and Collaboration 130 5.3.12 Targeted Recovery and Reconstruction 131 5.3.13 Enhanced Preparedness and Planning 131 5.4 Methodology 131 5.
4.1 Description of Research Design 132 5.4.2 Data Collection Methods 133 5.4.3 Analysis Techniques 134 5.5 Results 134 5.5.
1 Summary of Data Collected 135 5.5.2 Analysis of Data 136 5.5.3 Discussion of Findings 136 5.6 Discussion 139 5.6.1 Interpretation of Results 139 5.
6.2 Implications for the Future of Disaster Management 140 5.7 Conclusion 140 References 141 6 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 145 G. Vinuja and N. Bharatha Devi 6.1 Introduction 145 6.2 Literature Review 149 6.3 Experimental Methods and Materials 153 6.
3.1 Fast Data Transfer and Processing 153 6.3.2 Improved Accuracy and Precision in Monitoring 154 6.3.3 Enhanced Data Security and Privacy 155 6.4 Results and Discussion 156 6.4.
1 Innovative Remote Sensing Devices 156 6.4.2 Real-Time Monitoring Using Smart Sensors 157 6.4.3 Integration of 6G Technology and Artificial Intelligence 159 6.5 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 159 6.5.1 Soil and Water Quality Monitoring 160 6.
5.2 Climate and Weather Monitoring 160 6.5.3 Air Pollution Monitoring 161 6.6 Challenges and Limitations of Implementing 6G Technology in Environmental.