Cognitive Radio, Mobile Communications and Wireless Networks (eBook)

Mubashir Husain Rehmani (M’14-SM’15) received the B.Eng. degree in computer systems engineering from Mehran University of Engineering and Technology, Jamshoro, Pakistan, in 2004, the M.S. degree from the University of Paris XI, Paris, France, in 2008, and the Ph.D. degree from the University Pierre and Marie Curie, Paris, in 2011. He is currently working at the Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology (WIT), Waterford, Ireland. He served for five years as an Assistant Professor at COMSATS Institute of Information Technology, Wah Cantt., Pakistan.  He is currently an Area Editor of the IEEE Communications Surveys and Tutorials. He served for three years (from 2015 to 2017) as an Associate Editor of the IEEE Communications Surveys and Tutorials. Currently he serves as Associate Editor of  IEEE Communications Magazine, Elsevier Journal of Network and Computer Applications (JNCA), and the Journal of Communications and Networks (JCN). He is also serving as a Guest Editor of Elsevier Ad Hoc Networks journal, Elsevier Future Generation Computer Systems journal, the IEEE Transactions on Industrial Informatics, and Elsevier Pervasive and Mobile Computing journal. He has authored/ edited two books published by IGI Global, USA, one book published by CRC Press, USA, and one book with Wiley, U.K. He received “Best Researcher of the Year 2015 of COMSATS Wah” award in 2015. He received the certificate of appreciation, “Exemplary Editor of the IEEE Communications Surveys and Tutorials for the year 2015” from the IEEE Communications Society. He received Best Paper Award from IEEE ComSoc Technical Committee on Communications Systems Integration and Modeling (CSIM), in IEEE ICC 2017. He consecutively received research productivity award in 2016-17 and also ranked # 1 in all Engineering disciplines from Pakistan Council for Science and Technology (PCST), Government of Pakistan. He also received Best Paper Award in 2017 from Higher Education Commission (HEC), Government of Pakistan.Dr. Riadh Dhaou is an Associate Professor with the Toulouse INP (Institut National Polytechnique de Toulouse). He is affiliated with the Telecom and Networking department of the ENSEEIHT. Since 2003 he has been a member of the IRT team of the IRIT (Institut de Recherche en Informatique de Toulouse) Laboratory. He received a degree in Engineering in computer science (Diplome d'Ingénieur Concepteur en Informatique) from the ENSI (Ecole Nationale des Sciences de l'Informatique), University of Tunis II in 1997, and the D.E.A. (Diplome d'Etudes Approfondies) in Computer systems from the Université Pierre et Marie Curie in Paris (Paris VI), in 1998. He was awarded, respectively, a Ph.D. degree in Computer Systems, Telecommunication and Electronic by the University of Paris VI (in November 2002) and the HDR (Habilitation à Diriger des Recherches) by the Toulouse INP (in November 2017). His research interests include statistical characterization and modelling of mobility, mobile and space communications, cross layer schemes modelling and optimization, performance analysis of wireless networks, autonomous multi-hop/cooperative communications systems, capacity and outage analysis of multi-user heterogeneous wireless systems, resource allocation, design and performance evaluation of wireless sensor networks and energy consumption optimization. Since 2003, he is scientific chief project with the cooperative laboratory TéSA, a non-profit association, leading research studies and PhDs in Telecommunications for Space and Aeronautics. Since November 2017, he is the carrier of the satellite theme within the IRT team. He jointly supervised 14 Ph. D. Theses (9 were defended) and 3 master-degree theses. He published about 78 papers (7 journals and 5 book chapters) and achieved 35 research grants in satellite and sensor networks (CNES, Thales-Alenia Space, Airbus D&S). He has been technical leader to 7 research grants in satellite networks domain and participated to several industrial and academic grants. He was involved in the Technical Program Committee of 7 International Conferences. He was General Chair of PSATS'2013 and was member of one Organization Committee of two other International Conferences. He is, since 2013, part of the Editorial Board of WINET (The springer Wireless Networks journal). He participated to 11 PhD thesis committees. He participated to several European and National projects: CAPES-COFECUB Project MMAPS (Management, Mobility, Security, Architecture and Protocols for the Future Internet of Things) - ANR Project CAPTEURS - RNRT Project DILAN - ESPRIT Project BISANTE (Broadband Integrated Satellite Network Traffic Evaluation) - RNRT Project CONSTELLATIONS He also participated to the Network of Excellence NoE Euro-NGI, particularly on the evolution of the IP networks.

Contents 6
Author Biography 8
Chapter 1: Towards Spectrum Sharing in Virtualized Networks: A Survey and an Outlook 10
1.1 Introduction 10
1.2 Spectrum Sharing for 5G: An Overview 11
1.2.1 Exclusive Use of Spectrum (Individual Licenses) 13
1.2.2 License-Exempt Rules (Unlicensed or Commons) 14
1.2.3 Licensed Shared Access (LSA) and Authorized Shared Access (ASA) 14
1.2.4 Citizen Broadband Radio Service with Spectrum Access System 16
1.2.5 Pluralistic Licensing 16
1.2.6 Licensed Assisted Access (LAA) 17
1.2.7 Co-primary Shared Access 17
1.3 Legal Regulations for Spectrum Sharing 18
1.3.1 Spectrum Sharing Regulations in the USA 19
1.3.2 Spectrum Sharing Regulations in Europe 19
1.3.3 Spectrum Sharing Regulations Elsewhere 20
1.4 Trials 21
1.4.1 Licensed Shared Access (Authorized Shared Access) 21
1.4.2 Licensed Assisted Access (LAA) 22
1.4.3 Citizen Broadband Radio Service with Spectrum Access System (SAS) 22
1.5 Virtualization-Based Spectrum Sharing Solutions 23
1.5.1 An Overview of Existing Work on Virtualization in Wireless Networks 23
1.5.2 Overview of Existing Surveys on Cognitive Radio Networks 24
1.5.3 Spectrum Management Architecture 24
1.5.4 Abstractions for Spectrum Sharing 27
1.5.4.1 Exempt Use 27
Long-Term Information (Rarely Updated) 27
Short-Term Information (Permanent Monitoring) 27
1.5.4.2 License Exempt 28
Long-Term Information (Rarely Updated) 28
Short-Term Information (Permanent Monitoring) 28
1.5.4.3 Licensed Shared Access (LSA) 28
Long-Term Information (Rarely Updated) 29
Short-Term Information (Permanent Monitoring) 29
1.5.4.4 Spectrum Access System (SAS) 29
1.5.4.5 Pluralistic Licensing 29
Long-Term Information (Rarely Updated) 30
Short-Term Information (Permanent Monitoring) 30
1.5.4.6 License Assisted Access 30
1.5.4.7 Co-primary Shared Access 30
Long-Term Information (Rarely Updated) 30
Short-Term Information (Permanent Monitoring) 31
1.6 Key Challenges 31
1.6.1 Service Differentiation 31
1.6.2 Sharing of Information 32
1.6.3 Need for New Network Functions 32
1.6.4 Long-Term Contracts 32
1.6.5 Management and Control 33
1.6.6 Responsibility Assignment 33
1.7 Future Work and Conclusions 33
References 34
Chapter 2: Cloud-Based Context-Aware Spectrum Availability Monitoring and Prediction Using Crowd-Sensing 38
2.1 Introduction 38
2.2 Literature Review 40
2.2.1 Centralized Cooperative Spectrum Sensing and Decision-Making 40
2.2.2 Spectrum Monitoring with Crowd-Sensing 41
2.2.3 Spectrum Prediction 41
2.2.4 Cloud-Based Spectrum Monitoring 42
2.3 Proposed Method 43
2.3.1 System Model 43
2.3.2 Proposed Architecture 45
2.3.3 Spectrum and Contextual Sensors 46
2.3.4 Data Processing and Storage Units 47
2.3.5 Decision-Making Unit 48
2.4 Conclusion 51
2.5 Future Research Directions 52
References 52
Chapter 3: Cooperative Spectrum Handovers in Cognitive Radio Networks 55
3.1 Introduction 55
3.2 Literature Survey 57
3.3 Handover Procedure for CRN 59
3.3.1 Spectrum Management 60
3.3.1.1 Cooperative Detection 61
3.3.1.2 Noncooperative Detection 61
3.3.1.3 Spectrum Assignment 61
3.3.2 Spectrum Utilization 62
3.3.3 Sharing of Spectrum 62
3.4 The Proposed Cooperative Spectrum Handover 62
3.4.1 Threshold Optimization Based on Cooperative CUSUM 62
3.5 Cooperative Spectrum Sensing During Handover 63
3.5.1 Spectrum Sensing Approach 63
3.5.1.1 Algorithm for Spectrum Sensing 64
3.5.2 Energy Detection over AWGN Channels 65
3.5.3 Spectrum Detection 65
3.5.4 Probabilities of Detection 66
3.5.5 Signal-to-Noise Ratio Selection 67
3.5.6 Selection of Threshold 67
3.5.7 Cooperative CUSUM Algorithm 67
3.6 Evaluations and Discussion 68
3.7 Summary 69
References 69
Chapter 4: Network Coding-Based Broadcasting Schemes for Cognitive Radio Networks 72
4.1 Introduction 72
4.2 Cognitive Radio Network (CRN) 73
4.2.1 Definitions and Basic Concepts 73
4.2.2 Architecture 74
4.2.3 Fundamental Working Rules 75
4.2.4 Techniques 77
4.3 Broadcasting in CRN 78
4.3.1 Broadcasting Key Characteristics in CRN 79
4.4 Network Coding in CRN 80
4.4.1 Definitions and Basic Concepts 80
4.4.2 Network Coding Key Characteristics 82
4.5 Broadcasting in CRN 83
4.5.1 Broadcasting Protocols 84
4.5.2 Broadcast Schemes in CRN 86
4.5.2.1 Broadcasting over Randomly Selected Channel 86
4.5.2.2 Broadcasting over Common Control Channel 87
4.5.2.3 Metric-Based Broadcasting 88
4.5.2.4 Group-Based Broadcasting 89
4.5.2.5 Complete Broadcasting 90
4.5.2.6 Broadcast over Set of Channels 90
4.5.3 Issues and Challenges of Broadcasting in CRN 91
4.5.3.1 Channel Diversity and Heterogeneity 92
4.5.3.2 Agile Nature of Channel Availability 92
4.5.3.3 Common Control Channel Selection Challenges 92
4.5.3.4 Challenges Related to Neighbour Discovery 93
4.5.3.5 Challenges Related to Neighbour Channel Selection 93
4.5.3.6 Challenges Related to Collison Avoidance 93
4.5.3.7 Challenges Related to Route Selection 94
4.5.3.8 Challenges Caused by Rapid Channel Switching 94
4.6 Network Coding in CRN 94
4.6.1 Illustration of NC Using Simple Example 95
4.6.2 Classification of NC Schemes for CRN 96
4.6.2.1 Intersession NC Scheme 96
4.6.2.2 Intrasession NC Scheme 97
4.6.2.3 Linear NC Scheme 98
4.6.2.4 Random Linear NC Scheme 98
4.6.2.5 Physical Layer NC 99
4.6.2.6 Analogue NC Scheme 99
4.6.2.7 Rateless NC Scheme 100
4.6.2.8 Fountain Code NC Scheme 100
4.6.2.9 Asymmetric NC Scheme 100
4.6.2.10 Adaptive Dynamic NC Scheme 101
4.6.2.11 Differential Mesh Information Coding Scheme 101
4.6.2.12 Multiple Description NC Scheme 101
4.6.3 Cross-Layer Design of NC Schemes 102
4.6.3.1 Physical Layer NC (PHY-NC) 102
4.6.3.2 MAC Layer NC 103
4.6.3.3 Network Layer 103
4.6.3.4 Transport Layer NC 104
4.7 Network Coding-Based Broadcasting Techniques in CRN 105
4.7.1 Intersession NC 105
4.7.1.1 CODEB 105
4.7.1.2 CROR 106
4.7.1.3 Directional Antennas 107
4.7.1.4 Deadline Aware 107
4.7.2 Intrasession NC 110
4.7.2.1 Single Hop 110
4.7.2.2 Relay Aided 113
4.7.2.3 Multi-hop 114
References 116
Chapter 5: Cooperative and Cognitive Hybrid Satellite-Terrestrial Networks 122
5.1 Introduction 122
5.2 Multiuser Hybrid Satellite-Terrestrial Relay Network 126
5.2.1 System Model 126
5.2.2 Channel Models 128
5.2.3 Statistical Characterizations 129
5.2.4 Outage Performance Analysis 130
5.2.4.1 Exact Outage Probability 130
5.2.4.2 Asymptotic Outage Probability 132
5.2.5 Numerical Results 132
5.3 Multiuser Hybrid Cognitive Satellite-Terrestrial Network 134
5.3.1 System Model 134
5.3.2 Criteria for Secondary Network Selection 136
5.3.3 Channel Models 137
5.3.4 Performance Analysis of the Primary Network 138
5.3.4.1 For Direct Satellite (DS) Transmission Only 138
5.3.4.2 For Spectrum Sharing with DS Transmission 138
5.3.4.3 Constrained Power Allocation Policy for Spectrum Sharing 141
5.3.4.4 Numerical Results 141
5.3.5 Performance Analysis of Secondary Network 143
5.3.5.1 Outage Performance 143
5.3.5.2 Numerical Results 144
5.4 Issues and Challenges 145
5.5 Conclusion 146
References 146
Chapter 6: Health Monitoring Using Wearable Technologies and Cognitive Radio for IoT 150
6.1 Introduction 150
6.2 IoT and Medical Wearable Technologies: Perspective, Requirements and Limitations 152
6.2.1 Wearable Devices in Health Monitoring 152
6.2.2 Challenges and Bottlenecks for Medical IoT 153
6.2.3 Remote Health Monitoring 154
6.2.3.1 Pulse Sensors 155
6.2.3.2 Respiratory Rate Sensors 155
6.2.3.3 Body Temperature Sensors 155
6.2.3.4 Blood Pressure 155
6.2.3.5 Blood Oxygen 155
6.2.4 Communications Standards 156
6.2.4.1 Short-Range Communications 156
6.2.4.2 Long-Range Communications 157
6.3 Electromagnetic Interference for Medical Devices Connected Through IoT 157
6.4 Personal Health Monitoring in IoT: Requirements and Configurations 159
6.5 Cognitive Radio Modelling for IoT 161
6.6 Algorithms for Cognitive Radio Used in IoT for Medical Monitoring 164
6.6.1 Fuzzy Logic 165
6.6.2 Neural Networks 166
6.6.3 Genetic Algorithms 167
6.7 Cognitive Radio: Future Challenges of Personal Health Monitoring and IoT 167
6.8 Conclusions 169
References 170
Chapter 7: Millimeter Waves: Technological Component for Next-Generation Mobile Networks 173
7.1 Introduction 173
7.2 Applications of Millimeter Waves 175
7.3 Millimeter-Wave Frequency Spectrum 176
7.4 Characteristics of mmWaves 178
7.5 Contributions for Standardization of Channel Models for 5G Networks at mmWave Frequencies 180
7.6 Energy Efficiency in Networks Operating at mmWave Frequencies 182
7.7 Antenna Technology for 5G Systems at mmWave Frequencies 183
7.8 Cognitive Radios and mmWave Technology 184
7.9 Optimization in mmWave Networks 185
7.10 Projects Carried Out by Different Groups 185
7.11 Future Areas of Research for 5G Networks at mmWave Frequencies 186
7.12 Conclusion 187
References 188
Chapter 8: Spectrum Sensing in Cognitive Radio Networks Under Security Threats and Hybrid Spectrum Access 193
8.1 Introduction 193
8.2 System Model 195
8.3 Optimal Threshold Selection Approach 199
8.4 Throughput of Secondary User Under PUE Attack 200
8.5 Results and Discussions 206
8.6 Future Research Direction 210
8.7 Conclusions 210
References 211
Chapter 9: Optimum Spectrum Sensing Approaches in Cognitive Networks 214
9.1 Introduction 214
9.1.1 Spectrum Sensing in CRNs 216
9.2 Related Work 217
9.3 Optimized Spectrum Sensing Approaches 219
9.3.1 A Multilayered Framework for Optimal Sensing in Cognitive Ad Hoc Networks 219
9.3.1.1 Optimization at Level 1 220
Sensing the Spectrum Locally 220
Adaptive Threshold 220
9.3.1.2 Optimization at Level 2 222
Data Fusion 222
Optimal Number Estimator 222
Sensing Scheduler 223
9.3.1.3 Observations 223
9.3.2 Optimal Sensing Disruption for a CR Adversary 224
9.3.2.1 System Model 224
9.3.2.2 Optimal Sensing 225
9.3.2.3 Probability of False Detection 225
9.3.2.4 Optimal Sensing Disruption (Partial Band) 226
9.3.2.5 Observations 227
9.3.3 Parametric Optimization for Spectrum Sensing 227
9.3.3.1 Level 1 228
Interference Model 229
Optimization of Sensing Parameters 229
9.3.3.2 Level 2 230
9.3.3.3 Level 3 230
9.3.3.4 Observations 231
9.3.4 Cluster-Based Spectrum Sensing 231
9.4 Challenges and Future Scope 232
9.5 Conclusion 233
References 234
Chapter 10: Learning Strategies in Cognitive Radio Involving Soft Computing Techniques 237
10.1 Existing Scenario in Wireless Networks 237
10.2 Motivation 239
10.3 Need and Relevance 240
10.4 Fundamentals of Cognitive Radio 240
10.5 Cognitive Cycle 241
10.6 Artificial Intelligence and Soft Computing Techniques 242
10.7 Role of Soft Computing Techniques in Cognitive Engine 243
10.7.1 Spectrum Sensing 243
10.7.2 Cognitive Engine 245
10.7.3 Dynamic Spectrum Allocation 246
10.7.4 Significance of Learning in Cognitive Engine 248
10.7.5 Review on Learning Scheme for Cognitive Radio Using Soft Computing 249
10.7.6 Comparative Study and Summary 253
10.7.7 Gap Identification 254
10.7.8 Latest Contributions in Field of Learning in Cognitive Radio 254
10.7.9 Comparison of Different Networks Which Can Be Used for Learning 255
10.8 Conclusions 256
References 257
Chapter 11: Multiuser MIMO Cognitive Radio Systems 262
11.1 MU-MIMO Cognitive System 262
11.1.1 Gradient Search-Based Capacity-Aware Algorithm (GS-CA) 265
11.1.2 Performance Evaluation of CR-Based MU-MIMO 266
11.1.2.1 Symbol Error Rate and Ergodic Channel Capacity 267
11.1.2.2 Simulation Results 268
11.2 MU-MIMO in Cognitive Radio Wireless Sensor Networks 272
11.2.1 PSO-Based Capacity-Aware Algorithm (GS-CA) 272
11.2.2 Performance Evaluation of CR-WSNs 274
11.2.3 Energy Efficiency 276
11.3 Capacity-Aware Multiuser Massive MIMO for Heterogeneous Cellular Network 277
11.3.1 Performance Evaluation 281
11.4 Summary 282
References 282
Index 285