Spectrum Sharing in Cognitive Radio Networks

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.

• Preface xiiiSpecial Acknowledgements xxiList of Acronyms xxiiiList of Figures xxviiList of Tables xxxiiiList of Symbols xxxv1 Introduction 11.1 Introduction 11.1.1 Connected Environments 21.1.2 Evolution of Wireless Communication 51.1.3 Third Generation Partnership Project 101.2 Cognitive Radio Technology 101.2.1 Spectrum Accessing/Sharing Techniques 131.2.1.1 Interweave Spectrum Access 141.2.1.2 Underlay Spectrum Access 171.2.1.3 Overlay Spectrum Access 171.2.1.4 Hybrid Spectrum Access 171.3 Implementation of CR Networks 201.4 Motivation 221.5 Organization of Book 231.6 Summary 27References 272 Advanced Frame Structures in Cognitive Radio Networks 392.1 Introduction 392.2 Related Work 402.2.1 Frame Structures 402.2.2 Spectrum Accessing Strategies 412.3 Proposed Frame Structures for HSA Technique 432.4 Analysis of Throughput and Data Loss 452.5 Simulations and Results 472.6 Summary 50References 513 Cognitive Radio Network with Spectrum Prediction and MonitoringTechniques 553.1 Introduction 553.2 Related Work 573.2.1 Spectrum Prediction 573.2.2 Spectrum Monitoring 583.3 System Models 593.3.1 System Model for Approach-1 593.3.2 System Model for Approach-2 603.4 Performance Analysis 613.4.1 Throughput Analysis Using Approach-1 613.4.2 Analysis of Performance Metrics of the Approach-2 653.5 Results and Discussion 673.5.1 Proposed Approach-1 673.5.2 Proposed Approach-2 693.6 Summary 72References 724 Effect of Spectrum Prediction in Cognitive Radio Networks 774.1 Introduction 774.1.1 Spectrum Access Techniques 784.2 System Model 804.3 Throughput Analysis 874.4 Simulation Results and Discussion 894.5 Summary 93References 945 Effect of Imperfect Spectrum Monitoring on Cognitive RadioNetworks 975.1 Introduction 975.2 Related Work 995.2.1 Spectrum Sensing 995.2.2 Spectrum Monitoring 1005.3 System Model 1015.4 Performance Analysis of Proposed System Using Imperfect SpectrumMonitoring 1025.4.1 Computation of Ratio of the Achieved Throughput to Data Loss 1085.4.2 Computation of Power Wastage 1085.4.3 Computation of Interference Efficiency 1095.4.4 Computation of Energy Efficiency 1095.5 Results and Discussion 1105.6 Summary 115References 1166 Cooperative Spectrum Monitoring in Homogeneous andHeterogeneous Cognitive Radio Networks 1216.1 Introduction 1216.2 Background 1226.3 System Model 1246.4 Performance Analysis of Proposed CRN 1266.4.1 Computation of Achieved Throughput and Data Loss 1306.4.2 Computation of Interference Efficiency 1316.4.3 Computation of Energy Efficiency 1316.5 Results and Discussion 1326.5.1 Homogeneous Cognitive Radio Network 1326.5.2 Heterogeneous Cognitive Radio Networks 1346.6 Summary 143References 1437 Spectrum Mobility in Cognitive Radio Networks Using SpectrumPrediction and Monitoring Techniques 1477.1 Introduction 1477.2 System Model 1517.3 Performance Analysis 1537.4 Results and Discussion 1567.5 Summary 162References 1638 Hybrid Self-Scheduled Multichannel Medium Access Control Protocolin Cognitive Radio Networks 1678.1 Introduction 1678.2 Related Work 1698.2.1 CR-MAC Protocols 1698.2.2 Interference at PU 1718.3 System Model and Proposed Hybrid Self-Scheduled MultichannelMAC Protocol 1728.3.1 System Model 1728.3.2 Proposed HSMC-MAC Protocol 1738.4 Performance Analysis 1748.4.1 With Perfect Spectrum Sensing 1768.4.2 With Imperfect Spectrum Sensing 1788.4.3 More Feasible Scenarios 1808.5 Simulations and Results Analysis 1828.5.1 With Perfect Spectrum Sensing 1828.5.2 With Imperfect Spectrum Sensing 1858.6 Summary 190References 1909 Frameworks of Non-Orthogonal Multiple Access Techniques inCognitive Radio Networks 1959.1 Introduction 1959.1.1 Related Work 1969.1.2 Motivation 1999.1.3 Organization 1999.2 CR Spectrum Accessing Strategies 1999.3 Functions of NOMA System for Uplink and Downlink Scenarios 2049.3.1 Downlink Scenario for Cellular-NOMA 2049.3.2 Uplink Scenario for Cellular-NOMA 2079.4 Proposed Frameworks of CR with NOMA 2089.4.1 Framework-1 2099.4.2 Framework-2 2109.5 Simulation Environment and Results 2129.6 Research Potentials for NOMA and CR-NOMA Implementations 2139.6.1 Imperfect CSI 2149.6.2 Spectrum Hand-off Management 2159.6.3 Standardization 2159.6.4 Less Complex and Cost-Effective Systems 2159.6.5 Energy-Efficient Design and Frameworks 2169.6.6 Quality-of-Experience Management 2169.6.7 Power Allocation Strategy for CUs to Implement NOMA WithoutInterfering PU 2179.6.8 Cooperative CR-NOMA 2179.6.9 Interference Cancellation Techniques 2179.6.10 Security Aspects in CR-NOMA 2189.6.11 Role of User Clustering and Challenges 2189.6.12 Wireless Power Transfer to NOMA 2199.6.13 Multicell NOMA with Coordinated Multipoint Transmission 2209.6.14 Multiple-Carrier NOMA 2219.6.15 Cross-Layer Design 2219.6.16 MIMO-NOMA-CR 2229.7 Summary 222References 22310 Performance Analysis of MIMO-Based CR-NOMA CommunicationSystems 22910.1 Introduction 22910.2 Related Work for Several Combinations of CR, NOMA, and MIMOSystems 23110.3 System Model 23410.3.1 Downlink Scenarios 23610.3.2 Uplink Scenario 23810.4 Performance Analysis 23810.4.1 Downlink Scenario 23810.4.1.1 Throughput Computation for MIMO-CR-NOMA 23910.4.1.2 Throughput Computation for CR-NOMA Systems 24010.4.1.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, andCR-NOMA-MIMO Frameworks 24010.4.2 Uplink Scenario 24110.4.2.1 Throughput Computation for MIMO-CR-NOMA 24110.4.2.2 Throughput Calculation for CR-NOMA Systems 24210.4.2.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, andCR-NOMA-MIMO Frameworks 24210.4.2.4 Computation of Interference Efficiency of CU-4 In Case ofCR-MIMO-NOMA 24310.5 Simulation and Results Analysis 24310.5.1 Simulation Results for Downlink Scenario 24310.5.2 Simulation Results for Uplink Scenario 24510.6 Summary 249References 25011 Interference Management in Cognitive Radio Networks 25511.1 Introduction 25511.1.1 White space 25711.1.2 Grey Spaces 25711.1.3 Black Spaces 25711.1.4 Interference Temperature 25711.2 Interfering and Non-interfering CRN 25811.2.1 Interfering CRN 25811.2.2 Non-Interfering CRN 25911.3 Interference Cancellation Techniques in the CRN 26111.3.1 At the CU Transmitter 26111.3.2 At the CR-Receiver 26411.4 Cross-Layer Interference Mitigation in Cognitive Radio Networks 26811.5 Interference Management in Cognitive Radio Networks via CognitiveCycle Constituents 26911.5.1 Spectrum Sensing 26911.5.2 Spectrum Prediction 26911.5.3 Transmission Below PUs’ Interference Tolerable Limit 27111.5.4 Using Advanced Encoding Techniques 27111.5.5 Spectrum Monitoring 27211.6 Summary 274References 27412 Simulation Frameworks and Potential Research Challenges forInternet-of-Vehicles Networks 28112.1 Introduction 28112.1.1 Consumer IoT 28312.1.2 Industrial IoT 28312.2 Applications of CIoT 28412.2.1 Smart Home and Automation 28412.2.2 Smart Wearables 28412.2.3 Home Security and Smart Domestics 28512.2.4 Smart Farming 28512.3 Applications of Industrial IoT 28512.3.1 Smart Industry 28612.3.2 Smart Grid/Utilities 28612.3.3 Smart Communication 28612.3.4 Smart City 28712.3.5 Smart Energy Management 28712.3.6 Smart Retail Management 28812.3.7 Robotics 28812.3.8 Smart Cars/Connected Vehicles 28912.4 Communication Frameworks for IoVs 28912.4.1 Vehicle-to-Vehicle (V2V) Communication 29112.4.2 Vehicle to Infrastructure (V2I) Communication 29212.4.3 Infrastructure to Vehicles (I2V) Communication 29312.4.4 Vehicle-to-Broadband (V2B) Communication 29312.4.5 Vehicle-to-Pedestrians (V2P) Communication 29312.5 Simulation Environments for Internet-of-Vehicles 29512.5.1 SUMO 29612.5.2 Network Simulator (NetSim) 29612.5.3 Ns-2 29712.5.4 Ns-3 29712.5.5 OMNeT++ 29812.6 Potential Research Challenges 29912.6.1 Social Challenges 29912.6.2 Technical Challenges 30012.7 Summary 302References 30213 Radio Resource Management in Internet-of-Vehicles 31113.1 Introduction 31113.1.1 Dedicated Short-Range Communication 31313.1.2 Wireless Access for Vehicular Environments 31413.1.3 Communication Access for Land Mobile (CALM) 31413.2 Cellular Communication 31513.2.1 3GPP Releases 31513.2.2 Long-Term Evolution 31713.2.3 New Radio 31713.2.4 Dynamic Spectrum Access 31813.3 Role of Cognitive Radio for Spectrum Management 31913.4 Effect of Mobile Nature of Vehicles/Nodes on the Networking 32013.5 Spectrum Sharing in IoVs 32213.5.1 Spectrum Sensing Scenarios 32213.5.2 Spectrum Sharing Scenarios 32413.5.3 Spectrum Mobility/Handoff Scenarios 32513.6 Frameworks of Vehicular Networks with Cognitive Radio 32613.6.1 CR-Based IoVs Networks Architecture 32713.7 Key Potentials and Research Challenges 32813.7.1 Key Potentials 32813.7.2 Research Challenges 32913.8 Summary 333References 333Index 000