Spectrum Sharing The Next Frontier in Wireless Networks

Combines the latest trends in spectrum sharing, both from a research and a standards/regulation/experimental standpoint

Written by noted professionals from academia, industry, and research labs, this unique book provides a comprehensive treatment of the principles and architectures for spectrum sharing in order to help with the existing and future spectrum crunch issues. It presents readers with the most current standardization trends, including CEPT / CEE, eLSA, CBRS, MulteFire, LTE-Unlicensed (LTE-U), LTE WLAN integration with Internet Protocol security tunnel (LWIP), and LTE/Wi-Fi aggregation (LWA), and offers substantial trials and experimental results, as well as system-level performance evaluation results. The book also includes a chapter focusing on spectrum policy reinforcement and another on the economics of spectrum sharing.

Beginning with the historic form of cognitive radio, Spectrum Sharing: The Next Frontier in Wireless Networks continues with current standardized forms of spectrum sharing, and reviews all of the technical ingredients that may arise in spectrum sharing approaches. It also looks at policy and implementation aspects and ponders the future of the field. White spaces and data base-assisted spectrum sharing are discussed, as well as the licensed shared access approach and cooperative communication techniques. The book also covers reciprocity-based beam forming techniques for spectrum sharing in MIMO networks; resource allocation for shared spectrum networks; large scale wireless spectrum monitoring; and much more.

• Contains all the latest standardization trends, such as CEPT / ECC, eLSA, CBRS, MulteFire, LTE-Unlicensed (LTE-U), LTE WLAN integration with Internet Protocol security tunnel (LWIP) and LTE/Wi-Fi aggregation (LWA)
• Presents a number of emerging technologies for future spectrum sharing (collaborative sensing, cooperative communication, reciprocity-based beamforming, etc.), as well as novel spectrum sharing paradigms (e.g. in full duplex and radar systems)
• Includes substantial trials and experimental results, as well as system-level performance evaluation results
• Contains a dedicated chapter on spectrum policy reinforcement and one on the economics of spectrum sharing
• Edited by experts in the field, and featuring contributions by respected professionals in the field world wide

Spectrum Sharing: The Next Frontier in Wireless Networks is highly recommended for graduate students and researchers working in the areas of wireless communications and signal processing engineering. It would also benefit radio communications engineers and practitioners.

Constantinos B. Papadias, PhD, is Executive Director of Research, Technology and Innovation Network at The American College of Greece, Athens, Greece.

Tharmalingam Ratnarajah, PhD, is a Professor in Digital Communications and Signal Processing and Head of the Institute for Digital Communications at the University of Edinburgh, UK.

Dirk T.M. Slock, PhD, teaches Statistical Signal Processing (SSP) and signal processing techniques for wireless communications at EURECOM in France.

About the Editors xvii

List of Contributors xxi

Preface xxv

Abbreviations xxix

1 Introduction: From Cognitive Radio to Modern Spectrum Sharing 1
Constantinos B. Papadias, Tharmalingam Ratnarajah, and Dirk T.M. Slock

1.1 A Brief History of Spectrum Sharing 1

1.2 Background 3

1.3 Book overview 5

1.4 Summary 14

2 Regulation and Standardization Activities Related to Spectrum Sharing 17
Markus Mueck, María Dolores (Lola) Pérez Guirao, Rao Yallapragada, and Srikathyayani Srikanteswara

2.1 Introduction 17

2.2 Standardization 19

2.2.1 Licensed Shared Access 19

2.2.2 Evolved Licensed Shared Access 21

2.2.3 Citizen Broadband Radio System 24

2.2.4 CBRS Alliance 25

2.3 Regulation 28

2.3.1 European Conference of Postal and Telecommunications Administrations 28

2.3.2 Federal Communications Commission 29

2.3.3 A Comparison: (e)LSA vs CBRS Regulation Framework 30

2.3.4 Conclusion 31

References 32

3 White Spaces and Database-assisted Spectrum Sharing 35
Andrew Stirling

3.1 Introduction 35

3.2 Demand for Spectrum Outstrips Supply 36

3.2.1 Making Room for New Wireless Technology 36

3.2.2 Unused Spectrum 37

3.3 Three-tier Access Model 38

3.3.1 Secondary Users: Exploiting Gaps left by Primary Users 39

3.3.2 Passive Users: Vulnerable to Transmissions in White Space Frequencies 39

3.3.3 Opportunistic Spectrum Users 40

3.4 What is Efficient Use of Spectrum? 40

3.4.1 Broadcasters prefer Large Coverage Areas with Lower Spectrum Reuse 41

3.4.2 ISPs Respond to Growing Bandwidth Demand from Subscribers 41

3.4.3 Protection of Primary Users Defines the Scope for Sharing 42

3.5 Tapping Unused Capacity: the Evolution of Spectrum Sharing 43

3.5.1 Traditional Coordination is a Slow and Expensive Process 44

3.5.2 License-exempt Access as the Default Spectrum Sharing Mechanism 44

3.5.3 DSA offers Lower Friction and more Scalability 45

3.5.3.1 Early days of DSA 46

3.5.3.2 CR: Towards Flexible, Adaptive, Ad Hoc Access 46

3.5.4 Spectrum Databases are Preferred by Regulators 47

3.6 Determining which Frequencies are Available to Share: Technology 48

3.6.1 CR: Its Original Sense 48

3.6.2 DSA is more Pragmatic and Immediately Applicable 48

3.6.3 Spectrum Sensing 48

3.6.3.1 Hidden Nodes: Limiting the Scope/Certainty of Sensing 49

3.6.3.2 Overcoming the Hidden Node Problem: a Cooperative Approach 49

3.6.4 Beacons 50

3.6.5 Spectrum Databases used with Device Geolocation 51

3.7 Implementing Flexible Spectrum Access 53

3.7.1 Software-defined Radio Underpins Flexibility 53

3.7.2 Regulation Needs to Adapt to the New Flexibility in Radio Devices 54

3.8 Foundations for More Flexible Access in the Future 54

3.8.1 Finer-grained Spectrum Access Management 54

3.8.2 More Flexible License Exemption 54

3.8.2.1 Towards a UHF Spectrum Commons or Superhighway 55

References 56

Further Reading 57

4 Evolving Spectrum Sharing Methods, Standards and Trials: TVWS, CBRS, MulteFire and More 59
Dani Anderson, K.A. Shruthi, David Crawford, and Robert W. Stewart

4.1 Introduction 59

4.2 TV White Space 59

4.2.1 Overview 59

4.2.2 Operating Standards 61

4.2.3 Overview of TVWS Trials and Projects 63

4.3 Emerging Shared Spectrum Technologies 66

4.3.1 Introduction 66

4.3.2 CBRS 67

4.3.3 Other Shared Spectrum LTE Solutions 70

4.4 Conclusion 73

References 73

5 Spectrum Above Radio Bands 75
Abhishek K. Gupta and Adrish Banerjee

5.1 Introduction and Motivation for mmWave 75

5.2 mmWave Communication: What is Different? 76

5.2.1 Distinguishing Features 76

5.2.2 Implications 76

5.2.3 Opportunity and Need for Sharing 77

5.3 Bands in Above-6GHz Spectrum 78

5.3.1 26-GHz band: 24.25–27.5GHz 79

5.3.2 28-GHz band: 27.5–29.5GHz 79

5.3.3 32-GHz band: 31.8–33.4GHz 79

5.3.4 40-GHz band: 37–43.5GHz 79

5.3.4.1 40-GHz lower band 80

5.3.4.2 40-GHz upper band 80

5.3.5 64–71-GHz band 80

5.4 Spectrum Sharing over mmWave Bands 80

5.4.1 Factors Determining Sharing vs No Sharing 80

5.4.1.1 Directionality 81

5.4.1.2 Deployment and Blockage Density 81

5.4.1.3 Traffic Characteristics 82

5.4.1.4 Amount of Sharing 82

5.4.1.5 Inter-operator Coordination 82

5.4.1.6 Sharing of Other Resources 83

5.4.1.7 Multi-user Communication 84

5.4.1.8 Technical vs Financial Gains 84

5.5 Spectrum Sharing Options for mmWave Bands 84

5.5.1 Exclusive Licensing 84

5.5.2 Unlicensed Spectrum 85

5.5.2.1 Hybrid Spectrum Access 86

5.5.3 Spectrum License Sharing 87

5.5.3.1 Uncoordinated Sharing of Spectrum Licenses 87

5.5.3.2 Restricted Sharing of Spectrum Licenses 88

5.5.4 Shared Licenses 90

5.5.4.1 Spectrum Pooling 90

5.5.4.2 Partial or Fully Coordinated 90

5.5.4.3 Common Database 91

5.5.4.4 Sensing/D2D Communication-based Coordination 91

5.5.5 Secondary Licenses and Markets 91

5.5.5.1 Primary/Secondary Markets 92

5.5.5.2 Third-party Markets 92

5.5.6 Increasing the utilization of spectrum 92

5.6 Conclusions 93

References 93

6 The Licensed Shared Access Approach 97
António J. Morgado

6.1 Introduction to Spectrum Management 97

6.2 The Dawn of Licensed Shared Access 98

6.2.1 The LSA Regulatory Environment 99

6.2.2 LSA/ASA in the 2300–2400 MHz band 101

6.3 An Improved LSA Network Architecture 103

6.4 Operation of the Improved Architecture in Dynamic LSA Use Cases 106

6.4.1 Railway Scenario 107

6.4.2 Macro-cellular Scenario 109

6.4.3 Small Cell Scenario 112

6.5 Summary 115

References 116

7 Collaborative Sensing Techniques 121
Christian Steffens and Marius Pesavento

7.1 Sparse Signal Representation 123

7.2 Sparse Sensing 125

7.3 Collaborative Sparse Sensing 128

7.3.1 Coherent Sparse Reconstruction 129

7.3.2 Non-Coherent Sparse Reconstruction 131

7.4 Estimation Performance 134

7.4.1 Comparison of Centralized, Distributed, and Collaborative Sensing 134

7.4.2 Source Localization 136

7.5 Concluding Remarks 138

References 139

8 Cooperative Communication Techniques for Spectrum Sharing 147
Faheem Khan, Miltiades C. Filippou, and Mathini Sellathurai

8.1 Introduction 147

8.2 Distributed Precoding Exploiting Commonly Available Statistical CSIT for Efficient Coordination 149

8.2.1 Problem Formulation 150

8.2.2 Distributed Statistically Coordinated Precoding 151

8.2.3 Performance Evaluation 153

8.3 A Statistical Channel and Primary Traffic-aware Cooperation Framework for Optimal Service Coexistence 155

8.3.1 Joint Design of Spectrum Sensing and Reception for a SIMO Hybrid CR System 156

8.3.1.1 Problem Formulation and Solution Framework 158

8.3.1.2 Performance Evaluation 159

8.3.2 Throughput Performance of Sensing-optimized Hybrid MIMO CR Systems 161

8.3.2.1 Problem Formulation and Solution Framework 161

8.3.2.2 Performance Evaluation 162

8.4 Summary 164

References 165

9 Reciprocity-Based Beamforming Techniques for Spectrum Sharing in MIMO Networks 169
Kalyana Gopala and Dirk T.M. Slock

9.1 Multi-antenna Cognitive Radio Paradigms 169

9.1.1 Spatial Overlay: MISO/MIMO Interference Channel 170

9.1.2 Spatial Underlay 170

9.1.3 Spatial Interweave 170

9.2 From Multi-antenna Underlay to LSA Coordinated Beamforming 171

9.2.1 CoBF and CSIT Discussion 171

9.2.2 Some LoS Results 173

9.2.3 Noncoherent Multi-user MIMO Communications using Covariance CSIT 174

9.3 TDD Reciprocity Calibration 175

9.3.1 Fundamentals 175

9.3.2 Diagonality of the Calibration Matrix 178

9.3.3 Coherent and Non-coherent Calibration Scheme 178

9.3.4 UE-aided vs Internal Calibration 179

9.3.5 Group Calibration System Model 179

9.3.6 Least-squares Solution 181

9.3.7 A Bilinear Model 181

9.4 MIMO IBC Beamformer Design 182

9.4.1 System Model 182

9.4.2 WSR Optimization via WSMSE 182

9.4.3 Naive UL/DL Duality-based Beamformer Exploiting Reciprocity 183

9.5 Experimental Validation 184

9.6 Conclusions 188

References 188

10 Spectrum Sharing with Full Duplex 191
Sudip Biswas, Ali Cagatay Cirik, Miltiades C. Filippou, and Tharmalingam Ratnarajah

10.1 Introduction 191

10.2 Transceiver Design for an FD MIMO CR Cellular Network 192

10.2.1 System Model 192

10.2.1.1 Signal and Channel Model 192

10.2.1.2 SI Cancellation 194

10.2.1.3 MSE of the Received Data Stream 195

10.2.2 Joint Transceiver Design 196

10.2.3 Imperfect CSI and Robust Design 197

10.2.3.1 CSI Acquisition 197

10.2.3.2 CSI Modeling 198

10.2.3.3 Robust Transceiver Design 198

10.2.4 Numerical Results 200

10.3 Transceiver Design for an FD MIMO IoT Network 203

10.3.1 System Model 204

10.3.1.1 Signal and Channel Model 204

10.3.1.2 SI Cancellation 205

10.3.1.3 MSE of the Received Data Stream 206

10.3.2 Joint Transceiver Design 206

10.3.3 Imperfect CSI and Robust Design 207

10.3.4 Numerical Results 208

10.4 Summary 209

References 210

Appendix for Chapter 10 211

10.A.1 Useful lemmas 211

11 Communication and Radar Systems: Spectral Coexistence and Beyond 213
Fan Liu and Christos Masouros

11.1 Background and Applications 213

11.1.1 Civilian Applications 213

11.1.2 Military Applications 214

11.2 Radar Basics 214

11.3 Radar Communication Coexistence 216

11.3.1 Opportunistic Access 216

11.3.2 Precoding Designs 216

11.3.2.1 Interfering Channel Estimation 216

11.3.2.2 Closed-form Precoding 218

11.3.2.3 Optimization-based Precoding 219

11.4 Dual-functional Radar Communication Systems 221

11.4.1 Temporal and Spectral Processing 221

11.4.2 Spatial Processing 222

11.5 Summary and Open Problems 225

References 226

12 The Role of Antenna Arrays in Spectrum Sharing 229
Constantinos B. Papadias, Konstantinos Ntougias, and Georgios K. Papageorgiou

12.1 Introduction 229

12.2 Spectrum Sharing 229

12.2.1 Spectrum Sharing from a Physical Viewpoint 229

12.2.2 Spectrum Sharing from a Regulatory Viewpoint 231

12.3 Attributes of Antenna Arrays 233

12.4 Impact of Arrays on Spectrum Sharing 234

12.4.1 Spectrum Sensing 234

12.4.2 Shared Spectrum Access 234

12.5 Antenna-Array-Aided Spectrum Sharing 235

12.5.1 System Setup 235

12.5.2 Assumptions 236

12.5.3 System Model 237

12.5.3.1 Secondary System 237

12.5.3.2 Primary System 238

12.5.4 Problem Formulation 238

12.5.4.1 Sum-SE, SE, and SINR 238

12.5.4.2 Transmission Constraints 239

12.5.4.3 Original Optimization Problem 239

12.5.4.4 Relaxed Optimization Problem 240

12.5.5 Solution and Algorithm 242

12.5.5.1 Solution for Other Linear Precoding Schemes 242

12.5.6 Performance Evaluation via Numerical Simulations 243

12.6 Antenna-Array-Aided Spectrum Sensing 245

12.6.1 Printed Yagi–Uda Arrays and Hex-Antenna Nodes 246

12.6.2 Test Setup 248

12.6.3 Collaborative Spectrum Sensing Techniques 249

12.6.4 Experimental Results 250

12.6.4.1 Detection in High SNR 253

12.6.4.2 Detection in Low SNR 253

12.7 Summary and Conclusions 253

Acknowledgments 253

References 254

13 Resource Allocation for Shared Spectrum Networks 257
Eduard A. Jorswieck and M. Majid Butt

13.1 Introduction 257

13.2 Information-theoretic Background 259

13.3 Types of Spectrum Sharing 261

13.4 Resource Allocation for Efficient Spectrum Sharing 263

13.4.1 Multi-objective Programming 263

13.4.2 Resource Allocation Games 265

13.4.3 Resource Matching for Spectrum Sharing 267

13.5 Resource and Spectrum Trading 270

13.6 Conclusions and Future Work 275

References 275

14 Unlicensed Spectrum Access in 3GPP 279
Daniela Laselva, David López Pérez, Mika Rinne, Tero Henttonen, Claudio Rosa, Markku Kuusela

14.1 Introduction 279

14.2 LTE-WLAN Aggregation at the PDCP Layer 280

14.2.1 User Plane Radio Protocol Architecture 281

14.2.2 Bearer Type and Aggregation 282

14.2.3 Flow Control Schemes 283

14.3 LTE-WLAN Integration at IP Layer 284

14.3.1 User Plane Radio Protocol Architecture 284

14.3.2 Flow Control Schemes 286

14.4 LTE in Unlicensed Band 287

14.4.1 Spectrum and Regulations 287

14.4.2 Channel Access 288

14.4.3 Frame Structure 289

14.4.4 Discovery Reference Signal and RRM 290

14.4.5 Uplink Enhancements 291

14.5 Performance Evaluation 294

14.5.1 Aggregation Gains of LWA and LWIP 294

14.5.2 Performance Advantages of LAA 298

14.6 Future Technologies 301

14.6.1 5G New Radio in Unlicensed Band 301

14.6.2 The Role of WLAN in the 5G System 302

14.7 Conclusions 302

References 303

15 Performance Analysis of Spatial Spectrum Reuse in Ultradense Networks 305
Youjia Chen, Ming Ding, and David López-Pérez

15.1 Introduction 305

15.2 Network Scenario and System Model 306

15.2.1 Network Scenario 306

15.2.2 Wireless System Model 307

15.3 Performance Analysis of Full Spectrum Reuse Network 308

15.3.1 The Coverage Probability 308

15.3.2 The Area Spectral Efficiency 311

15.4 Performance with Multi-channel Spectrum Reuse 312

15.5 Simulation and Discussion 312

15.5.1 Performance with Full Spectrum Reuse Strategy 313

15.5.2 Performance with Multi-channel Spectrum Reuse Strategy 314

15.6 Conclusion 316

Appendix for Chapter 15 316

15.A.1 Proof of Lemma 15.1 316

15.A.2 Proof of Lemma 15.2 317

15.A.3 Proof of Theorem 15.1 318

References 318

16 Large-scale Wireless Spectrum Monitoring: Challenges and Solutions based on Machine Learning 321
Sreeraj Rajendran and Sofie Pollin

16.1 Challenges 321

16.2 Crowdsourcing 323

16.3 Wireless Spectrum Analysis 324

16.3.1 Anomaly Detection 324

16.3.2 Performance Comparisons 328

16.3.3 Wireless Signal Classification 331

16.3.3.1 Fully Supervised Models 331

16.3.3.2 Semi-supervised Models 332

16.3.3.3 Performance-friendly Models 333

16.4 Future Research Directions 335

16.4.1 Machine Learning 336

16.4.2 Anomaly Geo-localization 336

16.4.3 Crowd Engagement and Sustainability 336

16.5 Conclusion 337

References 337

17 Policy Enforcement in Dynamic Spectrum Sharing 341
Jung-Min (Jerry) Park, Vireshwar Kumar, and Taiwo Oyedare

17.1 Introduction 341

17.2 Technical Background 342

17.3 Security and Privacy Threats 343

17.3.1 Sensing-driven Spectrum Sharing 343

17.3.1.1 PHY-layer Threats 344

17.3.1.2 MAC-layer Threats 344

17.3.1.3 Cross-layer Threats 345

17.3.2 Database-driven Spectrum Sharing 345

17.3.2.1 PHY-layer Threats 346

17.3.2.2 Threats to the Database Access Protocol 346

17.3.2.3 Threats to the Privacy of Users 346

17.4 Enforcement Approaches 347

17.4.1 Ex Ante (Preventive) Approaches 348

17.4.1.1 Device Hardening 348

17.4.1.2 Network Hardening 350

17.4.1.3 Privacy Preservation 351

17.4.2 Ex Post (Punitive) Approaches 352

17.4.2.1 Spectrum Monitoring 352

17.4.2.2 Spectrum Forensics 352

17.4.2.3 Localization 353

17.4.2.4 Punishment 353

17.5 Open Problems 354

17.5.1 Research Challenges 354

17.5.2 Regulatory Challenges 354

17.6 Summary 355

References 355

18 Economics of Spectrum Sharing, Valuation, and Secondary Markets 361
William Lehr

18.1 Introduction 361

18.2 Spectrum Scarcity, Regulation, and Market Trends 363

18.3 Estimating Spectrum Values 370

18.4 Growing Demand for Spectrum 373

18.5 5G Future and Spectrum Economics 375

18.6 Secondary Markets and Sharing 381

18.7 Conclusion 384

References 385

19 The Future Outlook for Spectrum Sharing 389
Richard Womersley

19.1 Introduction 389

19.2 Share and Share Alike 390

19.3 Regulators Recognize the Value of Shared Access 393

19.4 The True Demand for Spectrum 395

19.5 The Impact of Sharing on Spectrum Demand 397

19.6 General Authorization needed to Encourage Sharing 399

19.7 The Long-term Outlook for Spectrum Sharing 401

References 403

Index 405

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