Rehmani / Dhaou Cognitive Radio, Mobile Communications and Wireless Networks

1. Auflage 2019
ISBN: 978-3-319-91002-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 292 Seiten

Reihe: EAI/Springer Innovations in Communication and Computing

This book provides an overview of the latest research and development of new technologies for cognitive radio, mobile communications, and wireless networks. The contributors discuss the research and requirement analysis and initial standardization work towards 5G cellular systems and the capacity problems it presents. They show how cognitive radio, with the capability to flexibly adapt its parameters, has been proposed as the enabling technology for unlicensed secondary users to dynamically access the licensed spectrum owned by legacy primary users on a negotiated or an opportunistic basis. They go on to show how cognitive radio is now perceived in a much broader paradigm that will contribute to solve the resource allocation problem that 5G requirements raise. The chapters represent hand-selected expanded papers from EAI sponsored and hosted conferences such as the 12th EAI International Conference on Mobile and Ubiquitous Systems, the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, the 10th International Conference on Cognitive Radio Oriented Wireless Networks, the 8th International Conference on Mobile Multimedia Communications, and the EAI International Conference on Software Defined Wireless Networks and Cognitive Technologies for IoT.


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.

Autoren/Hrsg.

Rehmani, Mubashir Husain

Dhaou, Riadh

Inhaltsverzeichnis

1;Contents;6
2;Author Biography;8
3;Chapter 1: Towards Spectrum Sharing in Virtualized Networks: A Survey and an Outlook;10
3.1;1.1 Introduction;10
3.2;1.2 Spectrum Sharing for 5G: An Overview;11
3.2.1;1.2.1 Exclusive Use of Spectrum (Individual Licenses);13
3.2.2;1.2.2 License-Exempt Rules (Unlicensed or Commons);14
3.2.3;1.2.3 Licensed Shared Access (LSA) and Authorized Shared Access (ASA);14
3.2.4;1.2.4 Citizen Broadband Radio Service with Spectrum Access System;16
3.2.5;1.2.5 Pluralistic Licensing;16
3.2.6;1.2.6 Licensed Assisted Access (LAA);17
3.2.7;1.2.7 Co-primary Shared Access;17
3.3;1.3 Legal Regulations for Spectrum Sharing;18
3.3.1;1.3.1 Spectrum Sharing Regulations in the USA;19
3.3.2;1.3.2 Spectrum Sharing Regulations in Europe;19
3.3.3;1.3.3 Spectrum Sharing Regulations Elsewhere;20
3.4;1.4 Trials;21
3.4.1;1.4.1 Licensed Shared Access (Authorized Shared Access);21
3.4.2;1.4.2 Licensed Assisted Access (LAA);22
3.4.3;1.4.3 Citizen Broadband Radio Service with Spectrum Access System (SAS);22
3.5;1.5 Virtualization-Based Spectrum Sharing Solutions;23
3.5.1;1.5.1 An Overview of Existing Work on Virtualization in Wireless Networks;23
3.5.2;1.5.2 Overview of Existing Surveys on Cognitive Radio Networks;24
3.5.3;1.5.3 Spectrum Management Architecture;24
3.5.4;1.5.4 Abstractions for Spectrum Sharing;27
3.5.4.1;1.5.4.1 Exempt Use;27
3.5.4.1.1;Long-Term Information (Rarely Updated);27
3.5.4.1.2;Short-Term Information (Permanent Monitoring);27
3.5.4.2;1.5.4.2 License Exempt;28
3.5.4.2.1;Long-Term Information (Rarely Updated);28
3.5.4.2.2;Short-Term Information (Permanent Monitoring);28
3.5.4.3;1.5.4.3 Licensed Shared Access (LSA);28
3.5.4.3.1;Long-Term Information (Rarely Updated);29
3.5.4.3.2;Short-Term Information (Permanent Monitoring);29
3.5.4.4;1.5.4.4 Spectrum Access System (SAS);29
3.5.4.5;1.5.4.5 Pluralistic Licensing;29
3.5.4.5.1;Long-Term Information (Rarely Updated);30
3.5.4.5.2;Short-Term Information (Permanent Monitoring);30
3.5.4.6;1.5.4.6 License Assisted Access;30
3.5.4.7;1.5.4.7 Co-primary Shared Access;30
3.5.4.7.1;Long-Term Information (Rarely Updated);30
3.5.4.7.2;Short-Term Information (Permanent Monitoring);31
3.6;1.6 Key Challenges;31
3.6.1;1.6.1 Service Differentiation;31
3.6.2;1.6.2 Sharing of Information;32
3.6.3;1.6.3 Need for New Network Functions;32
3.6.4;1.6.4 Long-Term Contracts;32
3.6.5;1.6.5 Management and Control;33
3.6.6;1.6.6 Responsibility Assignment;33
3.7;1.7 Future Work and Conclusions;33
3.8;References;34
4;Chapter 2: Cloud-Based Context-Aware Spectrum Availability Monitoring and Prediction Using Crowd-Sensing;38
4.1;2.1 Introduction;38
4.2;2.2 Literature Review;40
4.2.1;2.2.1 Centralized Cooperative Spectrum Sensing and Decision-Making;40
4.2.2;2.2.2 Spectrum Monitoring with Crowd-Sensing;41
4.2.3;2.2.3 Spectrum Prediction;41
4.2.4;2.2.4 Cloud-Based Spectrum Monitoring;42
4.3;2.3 Proposed Method;43
4.3.1;2.3.1 System Model;43
4.3.2;2.3.2 Proposed Architecture;45
4.3.3;2.3.3 Spectrum and Contextual Sensors;46
4.3.4;2.3.4 Data Processing and Storage Units;47
4.3.5;2.3.5 Decision-Making Unit;48
4.4;2.4 Conclusion;51
4.5;2.5 Future Research Directions;52
4.6;References;52
5;Chapter 3: Cooperative Spectrum Handovers in Cognitive Radio Networks;55
5.1;3.1 Introduction;55
5.2;3.2 Literature Survey;57
5.3;3.3 Handover Procedure for CRN;59
5.3.1;3.3.1 Spectrum Management;60
5.3.1.1;3.3.1.1 Cooperative Detection;61
5.3.1.2;3.3.1.2 Noncooperative Detection;61
5.3.1.3;3.3.1.3 Spectrum Assignment;61
5.3.2;3.3.2 Spectrum Utilization;62
5.3.3;3.3.3 Sharing of Spectrum;62
5.4;3.4 The Proposed Cooperative Spectrum Handover;62
5.4.1;3.4.1 Threshold Optimization Based on Cooperative CUSUM;62
5.5;3.5 Cooperative Spectrum Sensing During Handover;63
5.5.1;3.5.1 Spectrum Sensing Approach;63
5.5.1.1;3.5.1.1 Algorithm for Spectrum Sensing;64
5.5.2;3.5.2 Energy Detection over AWGN Channels;65
5.5.3;3.5.3 Spectrum Detection;65
5.5.4;3.5.4 Probabilities of Detection;66
5.5.5;3.5.5 Signal-to-Noise Ratio Selection;67
5.5.6;3.5.6 Selection of Threshold;67
5.5.7;3.5.7 Cooperative CUSUM Algorithm;67
5.6;3.6 Evaluations and Discussion;68
5.7;3.7 Summary;69
5.8;References;69
6;Chapter 4: Network Coding-Based Broadcasting Schemes for Cognitive Radio Networks;72
6.1;4.1 Introduction;72
6.2;4.2 Cognitive Radio Network (CRN);73
6.2.1;4.2.1 Definitions and Basic Concepts;73
6.2.2;4.2.2 Architecture;74
6.2.3;4.2.3 Fundamental Working Rules;75
6.2.4;4.2.4 Techniques;77
6.3;4.3 Broadcasting in CRN;78
6.3.1;4.3.1 Broadcasting Key Characteristics in CRN;79
6.4;4.4 Network Coding in CRN;80
6.4.1;4.4.1 Definitions and Basic Concepts;80
6.4.2;4.4.2 Network Coding Key Characteristics;82
6.5;4.5 Broadcasting in CRN;83
6.5.1;4.5.1 Broadcasting Protocols;84
6.5.2;4.5.2 Broadcast Schemes in CRN;86
6.5.2.1;4.5.2.1 Broadcasting over Randomly Selected Channel;86
6.5.2.2;4.5.2.2 Broadcasting over Common Control Channel;87
6.5.2.3;4.5.2.3 Metric-Based Broadcasting;88
6.5.2.4;4.5.2.4 Group-Based Broadcasting;89
6.5.2.5;4.5.2.5 Complete Broadcasting;90
6.5.2.6;4.5.2.6 Broadcast over Set of Channels;90
6.5.3;4.5.3 Issues and Challenges of Broadcasting in CRN;91
6.5.3.1;4.5.3.1 Channel Diversity and Heterogeneity;92
6.5.3.2;4.5.3.2 Agile Nature of Channel Availability;92
6.5.3.3;4.5.3.3 Common Control Channel Selection Challenges;92
6.5.3.4;4.5.3.4 Challenges Related to Neighbour Discovery;93
6.5.3.5;4.5.3.5 Challenges Related to Neighbour Channel Selection;93
6.5.3.6;4.5.3.6 Challenges Related to Collison Avoidance;93
6.5.3.7;4.5.3.7 Challenges Related to Route Selection;94
6.5.3.8;4.5.3.8 Challenges Caused by Rapid Channel Switching;94
6.6;4.6 Network Coding in CRN;94
6.6.1;4.6.1 Illustration of NC Using Simple Example;95
6.6.2;4.6.2 Classification of NC Schemes for CRN;96
6.6.2.1;4.6.2.1 Intersession NC Scheme;96
6.6.2.2;4.6.2.2 Intrasession NC Scheme;97
6.6.2.3;4.6.2.3 Linear NC Scheme;98
6.6.2.4;4.6.2.4 Random Linear NC Scheme;98
6.6.2.5;4.6.2.5 Physical Layer NC;99
6.6.2.6;4.6.2.6 Analogue NC Scheme;99
6.6.2.7;4.6.2.7 Rateless NC Scheme;100
6.6.2.8;4.6.2.8 Fountain Code NC Scheme;100
6.6.2.9;4.6.2.9 Asymmetric NC Scheme;100
6.6.2.10;4.6.2.10 Adaptive Dynamic NC Scheme;101
6.6.2.11;4.6.2.11 Differential Mesh Information Coding Scheme;101
6.6.2.12;4.6.2.12 Multiple Description NC Scheme;101
6.6.3;4.6.3 Cross-Layer Design of NC Schemes;102
6.6.3.1;4.6.3.1 Physical Layer NC (PHY-NC);102
6.6.3.2;4.6.3.2 MAC Layer NC;103
6.6.3.3;4.6.3.3 Network Layer;103
6.6.3.4;4.6.3.4 Transport Layer NC;104
6.7;4.7 Network Coding-Based Broadcasting Techniques in CRN;105
6.7.1;4.7.1 Intersession NC;105
6.7.1.1;4.7.1.1 CODEB;105
6.7.1.2;4.7.1.2 CROR;106
6.7.1.3;4.7.1.3 Directional Antennas;107
6.7.1.4;4.7.1.4 Deadline Aware;107
6.7.2;4.7.2 Intrasession NC;110
6.7.2.1;4.7.2.1 Single Hop;110
6.7.2.2;4.7.2.2 Relay Aided;113
6.7.2.3;4.7.2.3 Multi-hop;114
6.8;References;116
7;Chapter 5: Cooperative and Cognitive Hybrid Satellite-Terrestrial Networks;122
7.1;5.1 Introduction;122
7.2;5.2 Multiuser Hybrid Satellite-Terrestrial Relay Network;126
7.2.1;5.2.1 System Model;126
7.2.2;5.2.2 Channel Models;128
7.2.3;5.2.3 Statistical Characterizations;129
7.2.4;5.2.4 Outage Performance Analysis;130
7.2.4.1;5.2.4.1 Exact Outage Probability;130
7.2.4.2;5.2.4.2 Asymptotic Outage Probability;132
7.2.5;5.2.5 Numerical Results;132
7.3;5.3 Multiuser Hybrid Cognitive Satellite-Terrestrial Network;134
7.3.1;5.3.1 System Model;134
7.3.2;5.3.2 Criteria for Secondary Network Selection;136
7.3.3;5.3.3 Channel Models;137
7.3.4;5.3.4 Performance Analysis of the Primary Network;138
7.3.4.1;5.3.4.1 For Direct Satellite (DS) Transmission Only;138
7.3.4.2;5.3.4.2 For Spectrum Sharing with DS Transmission;138
7.3.4.3;5.3.4.3 Constrained Power Allocation Policy for Spectrum Sharing;141
7.3.4.4;5.3.4.4 Numerical Results;141
7.3.5;5.3.5 Performance Analysis of Secondary Network;143
7.3.5.1;5.3.5.1 Outage Performance;143
7.3.5.2;5.3.5.2 Numerical Results;144
7.4;5.4 Issues and Challenges;145
7.5;5.5 Conclusion;146
7.6;References;146
8;Chapter 6: Health Monitoring Using Wearable Technologies and Cognitive Radio for IoT;150
8.1;6.1 Introduction;150
8.2;6.2 IoT and Medical Wearable Technologies: Perspective, Requirements and Limitations;152
8.2.1;6.2.1 Wearable Devices in Health Monitoring;152
8.2.2;6.2.2 Challenges and Bottlenecks for Medical IoT;153
8.2.3;6.2.3 Remote Health Monitoring;154
8.2.3.1;6.2.3.1 Pulse Sensors;155
8.2.3.2;6.2.3.2 Respiratory Rate Sensors;155
8.2.3.3;6.2.3.3 Body Temperature Sensors;155
8.2.3.4;6.2.3.4 Blood Pressure;155
8.2.3.5;6.2.3.5 Blood Oxygen;155
8.2.4;6.2.4 Communications Standards;156
8.2.4.1;6.2.4.1 Short-Range Communications;156
8.2.4.2;6.2.4.2 Long-Range Communications;157
8.3;6.3 Electromagnetic Interference for Medical Devices Connected Through IoT;157
8.4;6.4 Personal Health Monitoring in IoT: Requirements and Configurations;159
8.5;6.5 Cognitive Radio Modelling for IoT;161
8.6;6.6 Algorithms for Cognitive Radio Used in IoT for Medical Monitoring;164
8.6.1;6.6.1 Fuzzy Logic;165
8.6.2;6.6.2 Neural Networks;166
8.6.3;6.6.3 Genetic Algorithms;167
8.7;6.7 Cognitive Radio: Future Challenges of Personal Health Monitoring and IoT;167
8.8;6.8 Conclusions;169
8.9;References;170
9;Chapter 7: Millimeter Waves: Technological Component for Next-Generation Mobile Networks;173
9.1;7.1 Introduction;173
9.2;7.2 Applications of Millimeter Waves;175
9.3;7.3 Millimeter-Wave Frequency Spectrum;176
9.4;7.4 Characteristics of mmWaves;178
9.5;7.5 Contributions for Standardization of Channel Models for 5G Networks at mmWave Frequencies;180
9.6;7.6 Energy Efficiency in Networks Operating at mmWave Frequencies;182
9.7;7.7 Antenna Technology for 5G Systems at mmWave Frequencies;183
9.8;7.8 Cognitive Radios and mmWave Technology;184
9.9;7.9 Optimization in mmWave Networks;185
9.10;7.10 Projects Carried Out by Different Groups;185
9.11;7.11 Future Areas of Research for 5G Networks at mmWave Frequencies;186
9.12;7.12 Conclusion;187
9.13;References;188
10;Chapter 8: Spectrum Sensing in Cognitive Radio Networks Under Security Threats and Hybrid Spectrum Access;193
10.1;8.1 Introduction;193
10.2;8.2 System Model;195
10.3;8.3 Optimal Threshold Selection Approach;199
10.4;8.4 Throughput of Secondary User Under PUE Attack;200
10.5;8.5 Results and Discussions;206
10.6;8.6 Future Research Direction;210
10.7;8.7 Conclusions;210
10.8;References;211
11;Chapter 9: Optimum Spectrum Sensing Approaches in Cognitive Networks;214
11.1;9.1 Introduction;214
11.1.1;9.1.1 Spectrum Sensing in CRNs;216
11.2;9.2 Related Work;217
11.3;9.3 Optimized Spectrum Sensing Approaches;219
11.3.1;9.3.1 A Multilayered Framework for Optimal Sensing in Cognitive Ad Hoc Networks;219
11.3.1.1;9.3.1.1 Optimization at Level 1;220
11.3.1.1.1;Sensing the Spectrum Locally;220
11.3.1.1.2;Adaptive Threshold;220
11.3.1.2;9.3.1.2 Optimization at Level 2;222
11.3.1.2.1;Data Fusion;222
11.3.1.2.2;Optimal Number Estimator;222
11.3.1.2.3;Sensing Scheduler;223
11.3.1.3;9.3.1.3 Observations;223
11.3.2;9.3.2 Optimal Sensing Disruption for a CR Adversary;224
11.3.2.1;9.3.2.1 System Model;224
11.3.2.2;9.3.2.2 Optimal Sensing;225
11.3.2.3;9.3.2.3 Probability of False Detection;225
11.3.2.4;9.3.2.4 Optimal Sensing Disruption (Partial Band);226
11.3.2.5;9.3.2.5 Observations;227
11.3.3;9.3.3 Parametric Optimization for Spectrum Sensing;227
11.3.3.1;9.3.3.1 Level 1;228
11.3.3.1.1;Interference Model;229
11.3.3.1.2;Optimization of Sensing Parameters;229
11.3.3.2;9.3.3.2 Level 2;230
11.3.3.3;9.3.3.3 Level 3;230
11.3.3.4;9.3.3.4 Observations;231
11.3.4;9.3.4 Cluster-Based Spectrum Sensing;231
11.4;9.4 Challenges and Future Scope;232
11.5;9.5 Conclusion;233
11.6;References;234
12;Chapter 10: Learning Strategies in Cognitive Radio Involving Soft Computing Techniques;237
12.1;10.1 Existing Scenario in Wireless Networks;237
12.2;10.2 Motivation;239
12.3;10.3 Need and Relevance;240
12.4;10.4 Fundamentals of Cognitive Radio;240
12.5;10.5 Cognitive Cycle;241
12.6;10.6 Artificial Intelligence and Soft Computing Techniques;242
12.7;10.7 Role of Soft Computing Techniques in Cognitive Engine;243
12.7.1;10.7.1 Spectrum Sensing;243
12.7.2;10.7.2 Cognitive Engine;245
12.7.3;10.7.3 Dynamic Spectrum Allocation;246
12.7.4;10.7.4 Significance of Learning in Cognitive Engine;248
12.7.5;10.7.5 Review on Learning Scheme for Cognitive Radio Using Soft Computing;249
12.7.6;10.7.6 Comparative Study and Summary;253
12.7.7;10.7.7 Gap Identification;254
12.7.8;10.7.8 Latest Contributions in Field of Learning in Cognitive Radio;254
12.7.9;10.7.9 Comparison of Different Networks Which Can Be Used for Learning;255
12.8;10.8 Conclusions;256
12.9;References;257
13;Chapter 11: Multiuser MIMO Cognitive Radio Systems;262
13.1;11.1 MU-MIMO Cognitive System;262
13.1.1;11.1.1 Gradient Search-Based Capacity-Aware Algorithm (GS-CA);265
13.1.2;11.1.2 Performance Evaluation of CR-Based MU-MIMO;266
13.1.2.1;11.1.2.1 Symbol Error Rate and Ergodic Channel Capacity;267
13.1.2.2;11.1.2.2 Simulation Results;268
13.2;11.2 MU-MIMO in Cognitive Radio Wireless Sensor Networks;272
13.2.1;11.2.1 PSO-Based Capacity-Aware Algorithm (GS-CA);272
13.2.2;11.2.2 Performance Evaluation of CR-WSNs;274
13.2.3;11.2.3 Energy Efficiency;276
13.3;11.3 Capacity-Aware Multiuser Massive MIMO for Heterogeneous Cellular Network;277
13.3.1;11.3.1 Performance Evaluation;281
13.4;11.4 Summary;282
13.5;References;282
14;Index;285