Sridhar Iyer (Senior Member, IEEE) is a Professor at KLE Technological University Dr MSSCET, India. His research interests include semantic communications and spectrum allocation for intelligent wireless systems. Anshuman Kalla (Senior Member, IEEE) is a Professor in the Department of Computer Engineering, CGPIT, Uka Tarsadia University (UTU), India. His research interests include blockchain and smart contract enabled systems, IoT, and next-generation mobile networks. Onel Alcaraz López (Member, IEEE) holds an Assistant Professorship (tenure track) in Sustainable Wireless Communications Engineering at the Centre for Wireless Communications (CWC), Oulu, Finland. His research interests include sustainable IoT, energy harvesting, wireless RF energy transfer, machine-type communications, and cellular-enabled positioning systems. Chamitha De Alwis (Senior Member, IEEE) is a Lecturer in Cybersecurity at the University of Bedfordshire, UK. His research interests include network security, 5G/6G technologies, and blockchain.

• About the Editors xiiiForeword xvPreface xixAcknowledgments xxiSection I 11 Evolution of Mobile Networks 3Deepak Kumar, Sridhar Iyer, and Onel Alcaraz López1.1 Introduction 31.2 Origins and Early Developments 41.3 Data-Centric Mobile Networks 81.4 5G Mobile Networks 111.5 Beyond 5G and Prospects 151.6 Conclusion 19References 192 Spectrum Access Options for Local 6G Networks 27Marja Matinmikko-Blue, Seppo Yrjölä, and Petri Ahokangas2.1 Introduction 272.2 Background/State of the art 282.2.1 Local Mobile Communication Networks 282.2.2 Spectrum Management for Local 5G Networks 292.2.2.1 Spectrum Management Approaches 292.2.2.2 The Role of Spectrum Sharing 312.2.2.3 Spectrum Access Options for Vertical Service Providers 322.3 Spectrum Valuation and Pricing 342.3.1 Spectrum Valuation in Mobile Communications 352.3.2 Spectrum Valuation Methods 402.4 Analysis of Identified Spectrum Access Options for Local 6G Networks 412.4.1 Local Licenses from the NRA 422.4.2 Local Spectrum Access Rights Acquired from Incumbent Spectrum User(s) 462.4.3 New Brokerage Models 472.4.4 Unlicensed Access 482.5 Conclusion 49Acknowledgment 50References 50Section II 553 Spectrum Management Technologies in Mobile Networks 57Harri Saarnisaari3.1 Background 573.2 Cell Frequency Planning 583.3 Steps Toward Dynamic Spectrum Access 603.3.1 LSA 603.3.2 CBRS 613.3.3 TVWS 613.3.4 Summary So Far 613.3.5 5G NR DSS 623.4 6G Spectrum Management Opportunities 623.4.1 6G Use Cases 623.4.2 6G Novelties 643.4.2.1 ISAC 653.4.2.2 RIS 653.4.2.3 AI 653.4.3 6G Spectrum 653.5 Way Ahead Toward 6G Spectrum Management 663.5.1 What Others Have Said? 663.5.2 Generic DSM Architecture for 6G 673.6 Conclusion 70References 704 Artificial Intelligence-Enabled Dynamic Spectrum Management 73Qiyang Zhao, Hang Zou, Yu Tian, Lina Bariah, Belkacem Mouhouche, Faouzi Bader, Ebtesam Almazrouei, and Merouane Debbah4.1 Introduction 734.2 Dynamic Spectrum Allocation 744.3 Machine Learning for Dynamic Spectrum Allocation 794.4 Large Language Models for Dynamic Spectrum Allocation 834.5 Challenges and Future Directions 854.6 Conclusion 87References 885 Infrastructure for Spectrum Management Enabled by Virtualization and Network Slicing 91Uditha Wijewardhana, Nishan Dharmaweera, and Bhathiya Pilanawithana5.1 Evolution of Network and Spectrum Management Infrastructure 915.1.1 Wired and Wireless Communication Systems 915.1.2 Requirement of Advanced Spectrum Management Infrastructure 925.2 Network Virtualization—Toward Software-Defined Networks 935.2.1 Foundations of Network Virtualization 935.2.2 Role of SDN in Network Virtualization 955.2.3 NFV: Complementing SDN in Network Virtualization 975.2.4 Challenges in SDN-Driven Network Virtualization 975.2.5 Emerging Trends and Developments 985.3 Network Slicing: A Pillar for Spectrum Management in Modern Networks 985.3.1 Definition and Overview of Network Slicing 985.3.2 Network Slicing: Components and Types 995.3.3 Benefits, Challenges, Threats, and Use Cases of Network Slicing 1025.3.4 Looking into Future of Network Slicing 1045.4 Network Virtualization and Network Slicing for Efficient Spectrum Management 1055.4.1 Integration of Network Virtualization and Slicing with Spectrum Management 1075.4.2 Spectrum Sharing, Allocation, and Dynamic Access: A Deeper Dive 1075.4.3 Use Cases: Virtualization and Slicing in Spectrum Management 1085.4.4 Quality of Service and Quality of Experience Improvements Through Virtualization and Slicing 1115.4.5 Security in Virtualized and Sliced Networks: Spectrum Management’s New Frontier 1125.4.6 Regulatory and Policy Implications in Spectrum Management for Virtualized and Sliced Networks 1135.4.7 Future Trends: AI and ML in Spectrum Management for Virtualized and Sliced Networks 1145.5 Spectrum Virtualization and Network Slicing Enabled Infrastructure for Spectrum Management 1165.5.1 Infrastructure Requirements for Spectrum Virtualization 1165.5.2 Virtualized Spectrum Management and Dynamic Spectrum Allocation in Modern Telecommunications 1185.5.3 Realizing the Future: Use Cases of Spectrum Virtualization and Network Slicing 1205.5.4 Scalability and Flexibility in Spectrum Virtualization: A Deep Dive into Modern Telecommunication Needs 1215.5.5 Securing the Future: Challenges and Enablers in Spectrum Virtualization 1225.5.6 Beyond the Horizon: Future Trends in Spectrum Management and Infrastructure 1245.6 Conclusion 125References 126Section III 1316 Spectrum Management for 6G RIS-SWIPT Systems 133Neha Sharma, Sumit Gautam, Prabhat Kumar Upadhyay, Symeon Chatzinotas, and Björn Ottersten6.1 Introduction 1336.1.1 Motivation 1356.2 Energy Harvesting Models 1366.2.1 Linear EH Model 1366.2.2 Constant-Linear EH Model 1376.2.3 Constant-Linear-Constant EH Model 1376.2.4 Non-linear EH Model 1376.3 Multiple RIS Scenario 1386.3.1 Multi-RIS Selection Strategies 1396.3.1.1 Exhaustive RIS Approach (ERA) 1396.3.1.2 Optimum RIS Approach (ORA) 1396.3.2 SWIPT Protocol 1406.4 Case Study 1416.4.1 Rate Maximization 1416.4.2 Simulation Setup 1426.4.3 Impact of Various RIS Arrangements 1426.4.3.1 Distributed or Collective RIS Elements: Which One to Choose? 1436.4.3.2 Comparing ERA and ORA Strategies 1456.4.3.3 Through the Lens of Outage Probability 1456.5 Spectrum Management 1456.6 Recent Advancements 1466.6.1 Beyond Diagonal RIS (BD-RIS) Systems 1466.6.2 RIS-Assisted Free Space Optics (FSO) Communication 1476.6.3 RIS-Assisted Vehicular-to-Everything (V2X) Communication 1476.6.4 RIS-Aided Integrated Sensing and Communications (ISAC) 1476.7 Conclusion 148References 1497 Reinforcement Learning and Deep Learning-Assisted Spectrum Management for RIS-SWIPT-Enabled 6G Systems 155Manojkumar B. Kokare, Purva Sharma, Swaminathan Ramabadran, Vimal Bhatia, and Sumit Gautam7.1 Introduction 1557.2 RIS Design and Characteristics 1587.3 SWIPT Protocols 1597.3.1 Rate Maximization via RIS 1617.3.2 Maximization of Total Harvested Energy via RIS 1627.3.3 Maximized Rate versus Maximum Power 1637.3.4 Maximized Harvested Energy Versus Maximum Power 1637.4 DRL in RIS-Aided 6G Wireless Communication Systems 1647.4.1 State-of-the-Art and Motivation 1647.4.2 DRL Framework for RIS-Assisted 6G Wireless Systems 1667.5 Open Issues and Challenges 1677.5.1 Spectrum Management 1687.5.2 Optimal RIS Placement 1687.5.3 Channel Estimation 1697.5.4 RIS Selection 1697.5.5 Security and Privacy 1707.6 Conclusion 170References 1718 RIS-Aided Low Complexity Waveform Design for Joint Sensing and Communications 175Christos Tsinos, Soumya P. Dash, Aryan Kaushik, Aakash Arora, and Marco Di Renzo8.1 Introduction 1758.2 RIS and ISAC 1788.2.1 Reconfigurable Intelligent Surfaces (RIS) 1788.2.2 Joint Radar-Communication (JRC) 1788.2.3 Integration of RIS and JRC 1808.3 Waveform Design for ISAC Systems 1818.3.1 Nonoverlapping Resource Allocation 1818.3.2 Fully Unified Waveforms 1838.4 Optimal Waveform Design for RIS-Assisted Mimo JRC System: A Case Study 1848.4.1 System Model 1848.4.1.1 Communication Model 1858.4.1.2 Radar Model 1898.4.2 Optimal Waveform Design for Non-RIS-Assisted System 1908.4.2.1 Simulation Results 1948.4.3 Optimal Waveform Design for RIS-Assisted System 1968.4.3.1 Simulation Results 1998.5 Conclusion 200References 202Section IV 2119 Blockchain and Smart Contract for Decentralized and Secure Spectrum Management Toward 6G – Beyond Hype 213Bikramjit Choudhury, Pranav K. Singh, Panchanan Nath, Ujjal Roy, and Anshuman Kalla9.1 Introduction 2139.2 Dynamic Spectrum Sharing, Blockchain, and Smart Contract 2189.2.1 Dynamic Spectrum Sharing 2189.2.2 Blockchain 2209.2.3 Smart Contract 2219.3 Blockchain and Smart Contract for Spectrum Sharing in 5G 2229.3.1 Related Works 2229.3.2 Summary of Major Gaps/Limitations 2249.4 Blockchain and Smart Contract for Spectrum Management in B5G and 6g 2269.4.1 Spectrum Management from B5G and 6G Perspective 2269.4.2 Blockchain and Smart Contract for Spectrum Allocation 2279.4.3 Blockchain and Smart Contract for Spectrum Sensing 2279.4.4 Blockchain and Smart Contract for Spectrum Sharing and Trading 2289.4.5 Blockchain and Smart Contract for Spectrum Access Coordination 2299.4.6 Blockchain and Smart Contract for Spectrum Regulation 2299.4.7 Blockchain and Smart Contract for Service-Level Agreements 2299.5 Deployment Challenges and Possible Solutions 2309.5.1 Regulation and Standardization 2309.5.2 Performance and Scalability 2319.5.3 AI Integration 2319.5.4 Interoperability 2329.5.5 Churn Management in Crowdsourcing 2329.5.6 Design and Security Issues of Smart Contracts 2329.5.7 Distributed Interference Management with Blockchain 2339.6 Conclusion 233References 23310 The Synergy of Artificial Intelligence and Blockchain in 6G Spectrum Management 237Ramalingam Murugan, Gokul Yenduri, Pyingkodi Maran, and Thippa Reddy Gadekallu10.1 Introduction 23710.1.1 General Challenges of Spectrum Usage and Management 23810.1.2 Importance of AI and Blockchain in Optimizing Spectrum Usage 23910.2 Understanding Spectrum Management in 6G 24110.2.1 The Evolving Requirements and Challenges of Spectrum Management in 6G 24110.2.1.1 Technology Integration 24210.2.1.2 Security Requirements 24210.2.2 The Need for Efficient and Dynamic Spectrum Allocation 24210.2.2.1 Escalating Demand for Bandwidth 24210.2.2.2 Diverse Use Cases 24310.2.2.3 Spectrum Scarcity 24310.2.2.4 Minimizing Interference 24310.2.2.5 Improved Spectral Efficiency 24310.3 Foundations of AI with Respect to 6G 24410.3.1 An Overview of AI Concepts Relevant to Spectrum Management 24410.4 Fundamentals of Blockchain with Respect to 6G 24710.4.1 Role of Blockchain in Spectrum Management 24710.4.2 Enhancement of Spectrum Management Using Blockchain 24710.5 AI-Driven Spectrum Prediction Techniques 24810.5.1 Data Collection 24910.5.2 AI Model Training 24910.5.3 Spectrum Forecasting 24910.5.4 Dynamic Spectrum Allocation 25010.5.5 Cognitive Radio Systems 25010.5.6 Spectrum Sharing 25010.5.7 AI-Driven Spectrum Optimization Techniques 25110.5.8 AI for Optimization of Spectrum Utilization and Management 25110.5.9 Reinforcement Learning 25210.5.10 Dynamic Programming Model 25210.5.11 Explainable AI 25210.5.12 Multiagent Systems 25310.6 Blockchain for Spectrum Access and Authentication 25310.6.1 Securing Spectrum Access and Authentication Using Blockchain Technologies 25410.6.2 Role of Smart Contract for Managing Spectrum Resources 25410.7 Synergy of AI and Blockchain in 6G 25610.7.1 A Deep Dive of AI Integration with Blockchain for 6G 25610.7.2 Amalgamation of Blockchain with AI for 6G Spectrum 25710.8 Conclusion 258References 25911 Conclusions 263Index 265