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Spectrum Sharing in Cognitive Radio Networks

Towards Highly Connected Environments

Thakur, Prabhat / Singh, Ghanshyam

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1. Edition August 2021
384 Pages, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-66542-7
John Wiley & Sons

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SPECTRUM SHARING IN COGNITIVE RADIO NETWORKS

Discover the latest advances in spectrum sharing in wireless networks from two internationally recognized experts in the field

Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments delivers an in-depth and insightful examination of hybrid spectrum access techniques with advanced frame structures designed for efficient spectrum utilization. The accomplished authors present the energy and spectrum efficient frameworks used in high-demand distributed architectures by relying on the self-scheduled medium access control (SMC-MAC) protocol in cognitive radio networks.

The book begins with an exploration of the fundamentals of recent advances in spectrum sharing techniques before moving onto advanced frame structures with spectrum accessing approaches and the role of spectrum prediction and spectrum monitoring to eliminate interference. The authors also cover spectrum mobility, interference, and spectrum management for connected environments in substantial detail.

Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments offers readers a recent and rational theoretical mathematical model of spectrum sharing strategies that can be used for practical simulation of future generation wireless communication technologies. It also highlights ongoing trends, revealing fresh research outcomes that will be of interest to active researchers in the area. Readers will also benefit from:
* An inclusive study of connected environments, 3GPP Releases, and the evolution of wireless communication generations with a discussion of advanced frame structures and access strategies in cognitive radio networks
* A treatment of cognitive radio networks using spectrum prediction and monitoring techniques
* An analysis of the effects of imperfect spectrum monitoring on cognitive radio networks
* An exploration of spectrum mobility in cognitive radio networks using spectrum prediction and monitoring techniques
* An examination of MIMO-based CR-NOMA communication systems for spectral and interference efficient designs

Perfect for senior undergraduate and graduate students in Electrical and Electronics Communication Engineering programs, Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments will also earn a place in the libraries of professional engineers and researchers working in the field, whether in private industry, government, or academia.

Preface xiii

Special Acknowledgements xxi

List of Acronyms xxiii

List of Figures xxvii

List of Tables xxxiii

List of Symbols xxxv

1 Introduction 1

1.1 Introduction 1

1.1.1 Connected Environments 2

1.1.2 Evolution of Wireless Communication 5

1.1.3 Third Generation Partnership Project 10

1.2 Cognitive Radio Technology 10

1.2.1 Spectrum Accessing/Sharing Techniques 13

1.2.1.1 Interweave Spectrum Access 14

1.2.1.2 Underlay Spectrum Access 17

1.2.1.3 Overlay Spectrum Access 17

1.2.1.4 Hybrid Spectrum Access 17

1.3 Implementation of CR Networks 20

1.4 Motivation 22

1.5 Organization of Book 23

1.6 Summary 27

References 27

2 Advanced Frame Structures in Cognitive Radio Networks 39

2.1 Introduction 39

2.2 Related Work 40

2.2.1 Frame Structures 40

2.2.2 Spectrum Accessing Strategies 41

2.3 Proposed Frame Structures for HSA Technique 43

2.4 Analysis of Throughput and Data Loss 45

2.5 Simulations and Results 47

2.6 Summary 50

References 51

3 Cognitive Radio Network with Spectrum Prediction and Monitoring

Techniques 55

3.1 Introduction 55

3.2 Related Work 57

3.2.1 Spectrum Prediction 57

3.2.2 Spectrum Monitoring 58

3.3 System Models 59

3.3.1 System Model for Approach-1 59

3.3.2 System Model for Approach-2 60

3.4 Performance Analysis 61

3.4.1 Throughput Analysis Using Approach-1 61

3.4.2 Analysis of Performance Metrics of the Approach-2 65

3.5 Results and Discussion 67

3.5.1 Proposed Approach-1 67

3.5.2 Proposed Approach-2 69

3.6 Summary 72

References 72

4 Effect of Spectrum Prediction in Cognitive Radio Networks 77

4.1 Introduction 77

4.1.1 Spectrum Access Techniques 78

4.2 System Model 80

4.3 Throughput Analysis 87

4.4 Simulation Results and Discussion 89

4.5 Summary 93

References 94

5 Effect of Imperfect Spectrum Monitoring on Cognitive Radio

Networks 97

5.1 Introduction 97

5.2 Related Work 99

5.2.1 Spectrum Sensing 99

5.2.2 Spectrum Monitoring 100

5.3 System Model 101

5.4 Performance Analysis of Proposed System Using Imperfect Spectrum

Monitoring 102

5.4.1 Computation of Ratio of the Achieved Throughput to Data Loss 108

5.4.2 Computation of Power Wastage 108

5.4.3 Computation of Interference Efficiency 109

5.4.4 Computation of Energy Efficiency 109

5.5 Results and Discussion 110

5.6 Summary 115

References 116

6 Cooperative Spectrum Monitoring in Homogeneous and

Heterogeneous Cognitive Radio Networks 121

6.1 Introduction 121

6.2 Background 122

6.3 System Model 124

6.4 Performance Analysis of Proposed CRN 126

6.4.1 Computation of Achieved Throughput and Data Loss 130

6.4.2 Computation of Interference Efficiency 131

6.4.3 Computation of Energy Efficiency 131

6.5 Results and Discussion 132

6.5.1 Homogeneous Cognitive Radio Network 132

6.5.2 Heterogeneous Cognitive Radio Networks 134

6.6 Summary 143

References 143

7 Spectrum Mobility in Cognitive Radio Networks Using Spectrum

Prediction and Monitoring Techniques 147

7.1 Introduction 147

7.2 System Model 151

7.3 Performance Analysis 153

7.4 Results and Discussion 156

7.5 Summary 162

References 163

8 Hybrid Self-Scheduled Multichannel Medium Access Control Protocol

in Cognitive Radio Networks 167

8.1 Introduction 167

8.2 Related Work 169

8.2.1 CR-MAC Protocols 169

8.2.2 Interference at PU 171

8.3 System Model and Proposed Hybrid Self-Scheduled Multichannel

MAC Protocol 172

8.3.1 System Model 172

8.3.2 Proposed HSMC-MAC Protocol 173

8.4 Performance Analysis 174

8.4.1 With Perfect Spectrum Sensing 176

8.4.2 With Imperfect Spectrum Sensing 178

8.4.3 More Feasible Scenarios 180

8.5 Simulations and Results Analysis 182

8.5.1 With Perfect Spectrum Sensing 182

8.5.2 With Imperfect Spectrum Sensing 185

8.6 Summary 190

References 190

9 Frameworks of Non-Orthogonal Multiple Access Techniques in

Cognitive Radio Networks 195

9.1 Introduction 195

9.1.1 Related Work 196

9.1.2 Motivation 199

9.1.3 Organization 199

9.2 CR Spectrum Accessing Strategies 199

9.3 Functions of NOMA System for Uplink and Downlink Scenarios 204

9.3.1 Downlink Scenario for Cellular-NOMA 204

9.3.2 Uplink Scenario for Cellular-NOMA 207

9.4 Proposed Frameworks of CR with NOMA 208

9.4.1 Framework-1 209

9.4.2 Framework-2 210

9.5 Simulation Environment and Results 212

9.6 Research Potentials for NOMA and CR-NOMA Implementations 213

9.6.1 Imperfect CSI 214

9.6.2 Spectrum Hand-off Management 215

9.6.3 Standardization 215

9.6.4 Less Complex and Cost-Effective Systems 215

9.6.5 Energy-Efficient Design and Frameworks 216

9.6.6 Quality-of-Experience Management 216

9.6.7 Power Allocation Strategy for CUs to Implement NOMA Without

Interfering PU 217

9.6.8 Cooperative CR-NOMA 217

9.6.9 Interference Cancellation Techniques 217

9.6.10 Security Aspects in CR-NOMA 218

9.6.11 Role of User Clustering and Challenges 218

9.6.12 Wireless Power Transfer to NOMA 219

9.6.13 Multicell NOMA with Coordinated Multipoint Transmission 220

9.6.14 Multiple-Carrier NOMA 221

9.6.15 Cross-Layer Design 221

9.6.16 MIMO-NOMA-CR 222

9.7 Summary 222

References 223

10 Performance Analysis of MIMO-Based CR-NOMA Communication

Systems 229

10.1 Introduction 229

10.2 Related Work for Several Combinations of CR, NOMA, and MIMO

Systems 231

10.3 System Model 234

10.3.1 Downlink Scenarios 236

10.3.2 Uplink Scenario 238

10.4 Performance Analysis 238

10.4.1 Downlink Scenario 238

10.4.1.1 Throughput Computation for MIMO-CR-NOMA 239

10.4.1.2 Throughput Computation for CR-NOMA Systems 240

10.4.1.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, and

CR-NOMA-MIMO Frameworks 240

10.4.2 Uplink Scenario 241

10.4.2.1 Throughput Computation for MIMO-CR-NOMA 241

10.4.2.2 Throughput Calculation for CR-NOMA Systems 242

10.4.2.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, and

CR-NOMA-MIMO Frameworks 242

10.4.2.4 Computation of Interference Efficiency of CU-4 In Case of

CR-MIMO-NOMA 243

10.5 Simulation and Results Analysis 243

10.5.1 Simulation Results for Downlink Scenario 243

10.5.2 Simulation Results for Uplink Scenario 245

10.6 Summary 249

References 250

11 Interference Management in Cognitive Radio Networks 255

11.1 Introduction 255

11.1.1 White space 257

11.1.2 Grey Spaces 257

11.1.3 Black Spaces 257

11.1.4 Interference Temperature 257

11.2 Interfering and Non-interfering CRN 258

11.2.1 Interfering CRN 258

11.2.2 Non-Interfering CRN 259

11.3 Interference Cancellation Techniques in the CRN 261

11.3.1 At the CU Transmitter 261

11.3.2 At the CR-Receiver 264

11.4 Cross-Layer Interference Mitigation in Cognitive Radio Networks 268

11.5 Interference Management in Cognitive Radio Networks via Cognitive

Cycle Constituents 269

11.5.1 Spectrum Sensing 269

11.5.2 Spectrum Prediction 269

11.5.3 Transmission Below PUs' Interference Tolerable Limit 271

11.5.4 Using Advanced Encoding Techniques 271

11.5.5 Spectrum Monitoring 272

11.6 Summary 274

References 274

12 Simulation Frameworks and Potential Research Challenges for

Internet-of-Vehicles Networks 281

12.1 Introduction 281

12.1.1 Consumer IoT 283

12.1.2 Industrial IoT 283

12.2 Applications of CIoT 284

12.2.1 Smart Home and Automation 284

12.2.2 Smart Wearables 284

12.2.3 Home Security and Smart Domestics 285

12.2.4 Smart Farming 285

12.3 Applications of Industrial IoT 285

12.3.1 Smart Industry 286

12.3.2 Smart Grid/Utilities 286

12.3.3 Smart Communication 286

12.3.4 Smart City 287

12.3.5 Smart Energy Management 287

12.3.6 Smart Retail Management 288

12.3.7 Robotics 288

12.3.8 Smart Cars/Connected Vehicles 289

12.4 Communication Frameworks for IoVs 289

12.4.1 Vehicle-to-Vehicle (V2V) Communication 291

12.4.2 Vehicle to Infrastructure (V2I) Communication 292

12.4.3 Infrastructure to Vehicles (I2V) Communication 293

12.4.4 Vehicle-to-Broadband (V2B) Communication 293

12.4.5 Vehicle-to-Pedestrians (V2P) Communication 293

12.5 Simulation Environments for Internet-of-Vehicles 295

12.5.1 SUMO 296

12.5.2 Network Simulator (NetSim) 296

12.5.3 Ns-2 297

12.5.4 Ns-3 297

12.5.5 OMNeT++ 298

12.6 Potential Research Challenges 299

12.6.1 Social Challenges 299

12.6.2 Technical Challenges 300

12.7 Summary 302

References 302

13 Radio Resource Management in Internet-of-Vehicles 311

13.1 Introduction 311

13.1.1 Dedicated Short-Range Communication 313

13.1.2 Wireless Access for Vehicular Environments 314

13.1.3 Communication Access for Land Mobile (CALM) 314

13.2 Cellular Communication 315

13.2.1 3GPP Releases 315

13.2.2 Long-Term Evolution 317

13.2.3 New Radio 317

13.2.4 Dynamic Spectrum Access 318

13.3 Role of Cognitive Radio for Spectrum Management 319

13.4 Effect of Mobile Nature of Vehicles/Nodes on the Networking 320

13.5 Spectrum Sharing in IoVs 322

13.5.1 Spectrum Sensing Scenarios 322

13.5.2 Spectrum Sharing Scenarios 324

13.5.3 Spectrum Mobility/Handoff Scenarios 325

13.6 Frameworks of Vehicular Networks with Cognitive Radio 326

13.6.1 CR-Based IoVs Networks Architecture 327

13.7 Key Potentials and Research Challenges 328

13.7.1 Key Potentials 328

13.7.2 Research Challenges 329

13.8 Summary 333

References 333

Index 000
Prabhat Thakur, PhD, is a Post-Doctoral Researcher in the Department of Electrical and Electronics Engineering Science, Faculty of Engineering and the Built Environment at the University of Johannesburg, South Africa. His research focus is on the energy, spectral, and interference efficient designs for spectrum sharing in cognitive radio communication systems.

Ghanshyam Singh, PhD, is Professor with the Department of Electrical and Electronics Engineering Science, APK Campus, at the University of Johannesburg, South Africa. He has authored or co-authored over 250 scientific papers.