About the Editors xiii
Foreword xv
Preface xix
Acknowledgments xxi
Section I 1
1 Evolution of Mobile Networks 3
Deepak Kumar, Sridhar Iyer, and Onel Alcaraz Lopez
1.1 Introduction 3
1.2 Origins and Early Developments 4
1.3 Data-Centric Mobile Networks 8
1.4 5G Mobile Networks 11
1.5 Beyond 5G and Prospects 15
1.6 Conclusion 19
References 19
2 Spectrum Access Options for Local 6G Networks 27
Marja Matinmikko-Blue, Seppo Yrjola, and Petri Ahokangas
2.1 Introduction 27
2.2 Background/State of the art 28
2.2.1 Local Mobile Communication Networks 28
2.2.2 Spectrum Management for Local 5G Networks 29
2.2.2.1 Spectrum Management Approaches 29
2.2.2.2 The Role of Spectrum Sharing 31
2.2.2.3 Spectrum Access Options for Vertical Service Providers 32
2.3 Spectrum Valuation and Pricing 34
2.3.1 Spectrum Valuation in Mobile Communications 35
2.3.2 Spectrum Valuation Methods 40
2.4 Analysis of Identified Spectrum Access Options for Local 6G Networks 41
2.4.1 Local Licenses from the NRA 42
2.4.2 Local Spectrum Access Rights Acquired from Incumbent Spectrum User(s) 46
2.4.3 New Brokerage Models 47
2.4.4 Unlicensed Access 48
2.5 Conclusion 49
Acknowledgment 50
References 50
Section II 55
3 Spectrum Management Technologies in Mobile Networks 57
Harri Saarnisaari
3.1 Background 57
3.2 Cell Frequency Planning 58
3.3 Steps Toward Dynamic Spectrum Access 60
3.3.1 LSA 60
3.3.2 CBRS 61
3.3.3 TVWS 61
3.3.4 Summary So Far 61
3.3.5 5G NR DSS 62
3.4 6G Spectrum Management Opportunities 62
3.4.1 6G Use Cases 62
3.4.2 6G Novelties 64
3.4.2.1 ISAC 65
3.4.2.2 RIS 65
3.4.2.3 AI 65
3.4.3 6G Spectrum 65
3.5 Way Ahead Toward 6G Spectrum Management 66
3.5.1 What Others Have Said? 66
3.5.2 Generic DSM Architecture for 6G 67
3.6 Conclusion 70
References 70
4 Artificial Intelligence-Enabled Dynamic Spectrum Management 73
Qiyang Zhao, Hang Zou, Yu Tian, Lina Bariah, Belkacem Mouhouche, Faouzi Bader, Ebtesam Almazrouei, and Merouane Debbah
4.1 Introduction 73
4.2 Dynamic Spectrum Allocation 74
4.3 Machine Learning for Dynamic Spectrum Allocation 79
4.4 Large Language Models for Dynamic Spectrum Allocation 83
4.5 Challenges and Future Directions 85
4.6 Conclusion 87
References 88
5 Infrastructure for Spectrum Management Enabled by Virtualization and Network Slicing 91
Uditha Wijewardhana, Nishan Dharmaweera, and Bhathiya Pilanawithana
5.1 Evolution of Network and Spectrum Management Infrastructure 91
5.1.1 Wired and Wireless Communication Systems 91
5.1.2 Requirement of Advanced Spectrum Management Infrastructure 92
5.2 Network Virtualization—Toward Software-Defined Networks 93
5.2.1 Foundations of Network Virtualization 93
5.2.2 Role of SDN in Network Virtualization 95
5.2.3 NFV: Complementing SDN in Network Virtualization 97
5.2.4 Challenges in SDN-Driven Network Virtualization 97
5.2.5 Emerging Trends and Developments 98
5.3 Network Slicing: A Pillar for Spectrum Management in Modern Networks 98
5.3.1 Definition and Overview of Network Slicing 98
5.3.2 Network Slicing: Components and Types 99
5.3.3 Benefits, Challenges, Threats, and Use Cases of Network Slicing 102
5.3.4 Looking into Future of Network Slicing 104
5.4 Network Virtualization and Network Slicing for Efficient Spectrum Management 105
5.4.1 Integration of Network Virtualization and Slicing with Spectrum Management 107
5.4.2 Spectrum Sharing, Allocation, and Dynamic Access: A Deeper Dive 107
5.4.3 Use Cases: Virtualization and Slicing in Spectrum Management 108
5.4.4 Quality of Service and Quality of Experience Improvements Through Virtualization and Slicing 111
5.4.5 Security in Virtualized and Sliced Networks: Spectrum Management’s New Frontier 112
5.4.6 Regulatory and Policy Implications in Spectrum Management for Virtualized and Sliced Networks 113
5.4.7 Future Trends: AI and ML in Spectrum Management for Virtualized and Sliced Networks 114
5.5 Spectrum Virtualization and Network Slicing Enabled Infrastructure for Spectrum Management 116
5.5.1 Infrastructure Requirements for Spectrum Virtualization 116
5.5.2 Virtualized Spectrum Management and Dynamic Spectrum Allocation in Modern Telecommunications 118
5.5.3 Realizing the Future: Use Cases of Spectrum Virtualization and Network Slicing 120
5.5.4 Scalability and Flexibility in Spectrum Virtualization: A Deep Dive into Modern Telecommunication Needs 121
5.5.5 Securing the Future: Challenges and Enablers in Spectrum Virtualization 122
5.5.6 Beyond the Horizon: Future Trends in Spectrum Management and Infrastructure 124
5.6 Conclusion 125
References 126
Section III 131
6 Spectrum Management for 6G RIS-SWIPT Systems 133
Neha Sharma, Sumit Gautam, Prabhat Kumar Upadhyay, Symeon Chatzinotas, and Bjorn Ottersten
6.1 Introduction 133
6.1.1 Motivation 135
6.2 Energy Harvesting Models 136
6.2.1 Linear EH Model 136
6.2.2 Constant-Linear EH Model 137
6.2.3 Constant-Linear-Constant EH Model 137
6.2.4 Non-linear EH Model 137
6.3 Multiple RIS Scenario 138
6.3.1 Multi-RIS Selection Strategies 139
6.3.1.1 Exhaustive RIS Approach (ERA) 139
6.3.1.2 Optimum RIS Approach (ORA) 139
6.3.2 SWIPT Protocol 140
6.4 Case Study 141
6.4.1 Rate Maximization 141
6.4.2 Simulation Setup 142
6.4.3 Impact of Various RIS Arrangements 142
6.4.3.1 Distributed or Collective RIS Elements: Which One to Choose? 143
6.4.3.2 Comparing ERA and ORA Strategies 145
6.4.3.3 Through the Lens of Outage Probability 145
6.5 Spectrum Management 145
6.6 Recent Advancements 146
6.6.1 Beyond Diagonal RIS (BD-RIS) Systems 146
6.6.2 RIS-Assisted Free Space Optics (FSO) Communication 147
6.6.3 RIS-Assisted Vehicular-to-Everything (V2X) Communication 147
6.6.4 RIS-Aided Integrated Sensing and Communications (ISAC) 147
6.7 Conclusion 148
References 149
7 Reinforcement Learning and Deep Learning-Assisted Spectrum Management for RIS-SWIPT-Enabled 6G Systems 155
Manojkumar B. Kokare, Purva Sharma, Swaminathan Ramabadran, Vimal Bhatia, and Sumit Gautam
7.1 Introduction 155
7.2 RIS Design and Characteristics 158
7.3 SWIPT Protocols 159
7.3.1 Rate Maximization via RIS 161
7.3.2 Maximization of Total Harvested Energy via RIS 162
7.3.3 Maximized Rate versus Maximum Power 163
7.3.4 Maximized Harvested Energy Versus Maximum Power 163
7.4 DRL in RIS-Aided 6G Wireless Communication Systems 164
7.4.1 State-of-the-Art and Motivation 164
7.4.2 DRL Framework for RIS-Assisted 6G Wireless Systems 166
7.5 Open Issues and Challenges 167
7.5.1 Spectrum Management 168
7.5.2 Optimal RIS Placement 168
7.5.3 Channel Estimation 169
7.5.4 RIS Selection 169
7.5.5 Security and Privacy 170
7.6 Conclusion 170
References 171
8 RIS-Aided Low Complexity Waveform Design for Joint Sensing and Communications 175
Christos Tsinos, Soumya P. Dash, Aryan Kaushik, Aakash Arora, and Marco Di Renzo
8.1 Introduction 175
8.2 RIS and ISAC 178
8.2.1 Reconfigurable Intelligent Surfaces (RIS) 178
8.2.2 Joint Radar-Communication (JRC) 178
8.2.3 Integration of RIS and JRC 180
8.3 Waveform Design for ISAC Systems 181
8.3.1 Nonoverlapping Resource Allocation 181
8.3.2 Fully Unified Waveforms 183
8.4 Optimal Waveform Design for RIS-Assisted Mimo JRC System: A Case Study 184
8.4.1 System Model 184
8.4.1.1 Communication Model 185
8.4.1.2 Radar Model 189
8.4.2 Optimal Waveform Design for Non-RIS-Assisted System 190
8.4.2.1 Simulation Results 194
8.4.3 Optimal Waveform Design for RIS-Assisted System 196
8.4.3.1 Simulation Results 199
8.5 Conclusion 200
References 202
Section IV 211
9 Blockchain and Smart Contract for Decentralized and Secure Spectrum Management Toward 6G – Beyond Hype 213
Bikramjit Choudhury, Pranav K. Singh, Panchanan Nath, Ujjal Roy, and Anshuman Kalla
9.1 Introduction 213
9.2 Dynamic Spectrum Sharing, Blockchain, and Smart Contract 218
9.2.1 Dynamic Spectrum Sharing 218
9.2.2 Blockchain 220
9.2.3 Smart Contract 221
9.3 Blockchain and Smart Contract for Spectrum Sharing in 5G 222
9.3.1 Related Works 222
9.3.2 Summary of Major Gaps/Limitations 224
9.4 Blockchain and Smart Contract for Spectrum Management in B5G and 6g 226
9.4.1 Spectrum Management from B5G and 6G Perspective 226
9.4.2 Blockchain and Smart Contract for Spectrum Allocation 227
9.4.3 Blockchain and Smart Contract for Spectrum Sensing 227
9.4.4 Blockchain and Smart Contract for Spectrum Sharing and Trading 228
9.4.5 Blockchain and Smart Contract for Spectrum Access Coordination 229
9.4.6 Blockchain and Smart Contract for Spectrum Regulation 229
9.4.7 Blockchain and Smart Contract for Service-Level Agreements 229
9.5 Deployment Challenges and Possible Solutions 230
9.5.1 Regulation and Standardization 230
9.5.2 Performance and Scalability 231
9.5.3 AI Integration 231
9.5.4 Interoperability 232
9.5.5 Churn Management in Crowdsourcing 232
9.5.6 Design and Security Issues of Smart Contracts 232
9.5.7 Distributed Interference Management with Blockchain 233
9.6 Conclusion 233
References 233
10 The Synergy of Artificial Intelligence and Blockchain in 6G Spectrum Management 237
Ramalingam Murugan, Gokul Yenduri, Pyingkodi Maran, and Thippa Reddy Gadekallu
10.1 Introduction 237
10.1.1 General Challenges of Spectrum Usage and Management 238
10.1.2 Importance of AI and Blockchain in Optimizing Spectrum Usage 239
10.2 Understanding Spectrum Management in 6G 241
10.2.1 The Evolving Requirements and Challenges of Spectrum Management in 6G 241
10.2.1.1 Technology Integration 242
10.2.1.2 Security Requirements 242
10.2.2 The Need for Efficient and Dynamic Spectrum Allocation 242
10.2.2.1 Escalating Demand for Bandwidth 242
10.2.2.2 Diverse Use Cases 243
10.2.2.3 Spectrum Scarcity 243
10.2.2.4 Minimizing Interference 243
10.2.2.5 Improved Spectral Efficiency 243
10.3 Foundations of AI with Respect to 6G 244
10.3.1 An Overview of AI Concepts Relevant to Spectrum Management 244
10.4 Fundamentals of Blockchain with Respect to 6G 247
10.4.1 Role of Blockchain in Spectrum Management 247
10.4.2 Enhancement of Spectrum Management Using Blockchain 247
10.5 AI-Driven Spectrum Prediction Techniques 248
10.5.1 Data Collection 249
10.5.2 AI Model Training 249
10.5.3 Spectrum Forecasting 249
10.5.4 Dynamic Spectrum Allocation 250
10.5.5 Cognitive Radio Systems 250
10.5.6 Spectrum Sharing 250
10.5.7 AI-Driven Spectrum Optimization Techniques 251
10.5.8 AI for Optimization of Spectrum Utilization and Management 251
10.5.9 Reinforcement Learning 252
10.5.10 Dynamic Programming Model 252
10.5.11 Explainable AI 252
10.5.12 Multiagent Systems 253
10.6 Blockchain for Spectrum Access and Authentication 253
10.6.1 Securing Spectrum Access and Authentication Using Blockchain Technologies 254
10.6.2 Role of Smart Contract for Managing Spectrum Resources 254
10.7 Synergy of AI and Blockchain in 6G 256
10.7.1 A Deep Dive of AI Integration with Blockchain for 6G 256
10.7.2 Amalgamation of Blockchain with AI for 6G Spectrum 257
10.8 Conclusion 258
References 259
11 Conclusions 263
Index 265
