Intelligent Spectrum Management
Description
Forward-thinking reference on spectrum sharing and resource management for 5G, B5G, and 6G wireless networks
Intelligent Spectrum Management: Towards 6G explores various aspects of spectrum sharing and resource management in 5G, beyond 5G, and the envisaged 6G networks. The book offers an in-depth exploration of intelligent and secure sharing of spectrum and resource management in existing and future mobile networks.
The book sets the stage by providing an insight to the evolution of mobile networks and highlights the importance of spectrum sharing and resource management in next-generation wireless networks. At the core, the book explores various promising technologies such as cognitive radio, reinforcement learning, deep learning, reconfigurable intelligent surfaces, and blockchain technology towards efficient, intelligent, and secure sharing of spectrum and resource management. Moreover, the book presents dynamic and decentralized resource management techniques, including network slicing, game theory, and blockchain-enabled approaches.
Topics covered include:
- Spectrum, and why it must be utilized optimally and transparently
- Future applications envisioned with 6G, such as digital twins, Industry 5.0, holographic telepresence, and Extended Reality (XR)
- Challenges when Dynamic Spectrum Management (DSM) is enabled through Machine Learning (ML) techniques, including the complexity of received signals and the difficulty in obtaining accurate network data such as channel state information
- Reinforcement learning and deep learning-assisted spectrum management
- Synergy between Artificial Intelligence (AI) and blockchain technology for spectrum management
- Private networks, including their prospects, architecture, enabling concepts, and techniques for efficient operation
In essence, various innovative technologies and approaches that can be leveraged to enhance spectrum utilization and efficiently manage network resources are discussed. The book is a potential reference for researchers, academics, and professionals in the wireless service provider industry, as well as regulators and officials.
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Persons
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.
Content
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 López
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 Yrjölä, 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 Björn 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
1
Evolution of Mobile Networks
Deepak Kumar1, Sridhar Iyer2, and Onel Alcaraz López1
1Centre for Wireless Communications (CWC), University of Oulu, Oulu, Finland
2Department of ECE, KLE Technological University, Dr. MSSCET, Belagavi, Karnataka, India
1.1 Introduction
The advent of mobile networks has ushered in a new era of communication. This in turn is playing a pivotal role in our interconnected world and has transformed the way we connect, work, and live. By enabling people to seamlessly communicate, access information, and utilize digital services anytime and anywhere, mobile networks increasingly bridge the digital divide and support businesses, e-commerce, and digital entrepreneurship, thus fueling economic growth and societal advances. Indeed, mobile communications can significantly contribute to achieving the sustainable development goals of the United Nations.1 This is possible by offering infrastructure and access to digital services supporting growth, efficiency, and sustainability, especially for economies where existing services are limited or the related infrastructure is poor [1].
Digitalization of services delivered through mobile communications networks has shown immense benefit in developing economies in particular driving the uptake of micro-banking and micro-finance, micro-energy grids, and market creation [2]. Productivity enhancement may arise from remote work support and access to critical information and digital services, a capability that has become crucial in the face of global challenges such as the COVID-19 pandemic. In times of crisis, mobile networks even serve as vital lifelines, facilitating emergency communication and coordination.
There have been phenomenal advancements in mobile communications for more than a century and consistently since the first-generation (1G) networks in the 1980s. A new generation has been introduced nearly every 10 years since then, with each iteration bringing significant improvements in terms of key performance indicators (KPIs). From the initial 1G networks that allowed only voice calls, mobile communication networks have progressed through the second generation (2G), third generation (3G), fourth generation (4G), and fifth generation (5G), enabling not only voice communication but also high-speed Internet access, mobile gaming, location tracking, online education, augmented reality applications, and foundation for the Internet of Things (IoT) [3]. A graphical representation of the evolution of mobile networks, including some distinctive KPIs that emerged during the evolution, is shown in Figure 1.1.
Figure 1.1 Evolution of mobile networks and emerging KPIs.
Understanding the evolution of mobile networks is of paramount importance for researchers, engineers, and academics, and constitutes the scope of this chapter. Such an understanding/knowledge drives innovation, informs policy, opens business avenues, and ensures the education of future generations. As mobile networks continue to evolve, staying informed and engaged in their development is vital for the betterment of society and the advancement of human, human-to-machine, and machine-to-machine connectivity.
1.2 Origins and Early Developments
Telecommunications have been critical to human society since ancient times. The discovery of the relationship between electricity and magnetism by Hans Christian Oersted in 1820 was a milestone. Further, Michael Faraday showed that a fluctuating magnetic field could induce electric current on a conductor in 1831, marking a turning point in the history of telecommunications by laying the foundation for subsequent advancements. This discovery laid the groundwork for wireless communication at non-line-of-sight distances.
Later on, two major telecommunication developments were the invention of the telephone by Alexander Graham Bell in 1876, allowing the transmission of analog live signals, and Marconi's wireless telegraphy in 1896. The first amplitude-modulation radio broadcasting was demonstrated by Reginald A. Fessenden in 1906. Meanwhile, in 1935, Major Edwin H. Armstrong demonstrated high-quality music transmission using frequency-modulation radio broadcasts. The private telephone companies also started providing landline telephones and services. During the 1940s, the general public was offered a public mobile telephone, which was a wireless device that connected with the public switched telephone network [4]. The United States' Federal Communication Commission (FCC) started two-way radio service over a 460 MHz band in 1945, where millions of users used the same channel across the country. In 1947, radiotelephone systems, comprising small geographical areas called cells, were proposed by the Bell laboratories [5]. A base station (BS) transmitter setup was placed in each cell, and cell traffic was controlled by a central switch. A few decades later, in 1971, the first wireless computer network, called AlohaNet, was introduced [6]. AlohaNet could connect multiple low-data rate stations via a single radio channel to a central host without considering any access rule or synchronization [7]. In 1972, S-Aloha was proposed using a time-slotted channel, which could double the channel capacity. The cellular radio networks assign radio channels to mobile stations by employing a demand-assigned multiple-access protocol, where the uplink request channel is based on S-Aloha.
The initial cellular systems are referred to as mobile radiotelephone or zeroth generation (0G), pointing to the pre-cellphone mobile telephony. Such systems were usually mounted in vehicles' boot/trunk. Indeed, the transceiver was mounted in the vehicle boot and was usually placed on the head section and fixed close to the driver's seat. In 1979, Nippon Telegraph and Telephone Corporation launched 1G to the citizens of Tokyo, which was available nationwide in Japan by 1984 [8]. The cellular standards used in 1G included mainly Advanced Mobile Phone Services (AMPS) and Nordic Mobile Telephones (NMT). The AMPS was invented at Bell Labs [9] and also used in the United Kingdom under the name of Total Access Communication System [10]. The NMT system was simultaneously introduced in Denmark, Finland, Norway, and Sweden in 1981, which was the first mobile phone network with worldwide roaming [11]. Motorola's DynaTAC mobile phone was implemented by Chicago-based Ameritech in 1983 to build the first 1G network in the United States [12]. The world's first cell phone, DynaTAC 8000X, created by Motorola in 1983, was essentially a two-way frequency-modulation radio designed only for voice calls [13]. Several nations, including the United Kingdom, Mexico, and Canada, followed in the early to mid-1980s.
1G was completely an analog cellular system. The fundamental concept underlying 1G cellular networks is the division of the region into cells, each of which is serviced by a BS and usually spans 10-25 km. The small size of cells allows for frequency reuse in nearby cells. In addition, smaller cells need smaller, less-expensive, and less-powerful equipment to send and receive data. However, these standards did not include any placement (i.e., coordinates of a certain object) instructions. The utilization of location data within the network garnered noteworthy interest from both, operators and application developers. Vehicle location (by using signal strength, time delay, or direction of arrival measurements) was targeted to improve the effectiveness of cellular calls through system control [14]. 1G cellular systems were additionally utilized for intelligent vehicle highway system (IVHS) applications [15]. Further, it supported emergency services based on proprietary location solutions. This included services such as those of Grayson Wireless with a joint time difference of arrival and angle of arrival solution or true position with an uplink time difference of arrival, both of which used AMPS signals [16]. 1G networks realized data rates up to 2.4 kbps, sufficient to support analog voice call services [10]. Meanwhile, these networks were concentrated in urban and densely populated areas with limited coverage and capacity, and faced network congestion, excessive call drop problems, reckless hand-off, and security threats.
The Groupe Spécial Mobile, established in 1982 by the European Conference of Postal and Telecommunications Administrations, started working on the harmonization of mobile communication systems in the 900 MHz band [9]. The primary service was telephony; however, it was also possible to use enticing nonvoice alternatives. The action plan included studies on market, tariff, technical, and regulatory needs. Further, in 1984, France and Germany contributed to the global system for mobile communications (GSM) [17]. A structure for the GSM specifications and an action plan for their completion was created. It was decided to employ smart cards as subscriber identity modules (SIMs), and support free circulation, mobile station licensing, international roaming, charging, and accounting. At a high-level meeting in Bonn in May 1987, the basic GSM specification was finally agreed upon.
The transition from 1G to 2G in the 1990s constituted the transition from analog to digital cellular systems, offering improved voice quality and paving the way for sophisticated mobile services and capabilities. The progression of mobile communications from several separate...
