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 xviiList of Contributors xxiPreface xxvAbbreviations xxix1 Introduction: From Cognitive Radio to Modern Spectrum Sharing 1Constantinos B. Papadias, Tharmalingam Ratnarajah, and Dirk T.M. Slock1.1 A Brief History of Spectrum Sharing 11.2 Background 31.3 Book overview 51.4 Summary 142 Regulation and Standardization Activities Related to Spectrum Sharing 17Markus Mueck, María Dolores (Lola) Pérez Guirao, Rao Yallapragada, and Srikathyayani Srikanteswara2.1 Introduction 172.2 Standardization 192.2.1 Licensed Shared Access 192.2.2 Evolved Licensed Shared Access 212.2.3 Citizen Broadband Radio System 242.2.4 CBRS Alliance 252.3 Regulation 282.3.1 European Conference of Postal and Telecommunications Administrations 282.3.2 Federal Communications Commission 292.3.3 A Comparison: (e)LSA vs CBRS Regulation Framework 302.3.4 Conclusion 31References 323 White Spaces and Database-assisted Spectrum Sharing 35Andrew Stirling3.1 Introduction 353.2 Demand for Spectrum Outstrips Supply 363.2.1 Making Room for New Wireless Technology 363.2.2 Unused Spectrum 373.3 Three-tier Access Model 383.3.1 Secondary Users: Exploiting Gaps left by Primary Users 393.3.2 Passive Users: Vulnerable to Transmissions in White Space Frequencies 393.3.3 Opportunistic Spectrum Users 403.4 What is Efficient Use of Spectrum? 403.4.1 Broadcasters prefer Large Coverage Areas with Lower Spectrum Reuse 413.4.2 ISPs Respond to Growing Bandwidth Demand from Subscribers 413.4.3 Protection of Primary Users Defines the Scope for Sharing 423.5 Tapping Unused Capacity: the Evolution of Spectrum Sharing 433.5.1 Traditional Coordination is a Slow and Expensive Process 443.5.2 License-exempt Access as the Default Spectrum Sharing Mechanism 443.5.3 DSA offers Lower Friction and more Scalability 453.5.3.1 Early days of DSA 463.5.3.2 CR: Towards Flexible, Adaptive, Ad Hoc Access 463.5.4 Spectrum Databases are Preferred by Regulators 473.6 Determining which Frequencies are Available to Share: Technology 483.6.1 CR: Its Original Sense 483.6.2 DSA is more Pragmatic and Immediately Applicable 483.6.3 Spectrum Sensing 483.6.3.1 Hidden Nodes: Limiting the Scope/Certainty of Sensing 493.6.3.2 Overcoming the Hidden Node Problem: a Cooperative Approach 493.6.4 Beacons 503.6.5 Spectrum Databases used with Device Geolocation 513.7 Implementing Flexible Spectrum Access 533.7.1 Software-defined Radio Underpins Flexibility 533.7.2 Regulation Needs to Adapt to the New Flexibility in Radio Devices 543.8 Foundations for More Flexible Access in the Future 543.8.1 Finer-grained Spectrum Access Management 543.8.2 More Flexible License Exemption 543.8.2.1 Towards a UHF Spectrum Commons or Superhighway 55References 56Further Reading 574 Evolving Spectrum Sharing Methods, Standards and Trials: TVWS, CBRS, MulteFire and More 59Dani Anderson, K.A. Shruthi, David Crawford, and Robert W. Stewart4.1 Introduction 594.2 TV White Space 594.2.1 Overview 594.2.2 Operating Standards 614.2.3 Overview of TVWS Trials and Projects 634.3 Emerging Shared Spectrum Technologies 664.3.1 Introduction 664.3.2 CBRS 674.3.3 Other Shared Spectrum LTE Solutions 704.4 Conclusion 73References 735 Spectrum Above Radio Bands 75Abhishek K. Gupta and Adrish Banerjee5.1 Introduction and Motivation for mmWave 755.2 mmWave Communication: What is Different? 765.2.1 Distinguishing Features 765.2.2 Implications 765.2.3 Opportunity and Need for Sharing 775.3 Bands in Above-6GHz Spectrum 785.3.1 26-GHz band: 24.25–27.5GHz 795.3.2 28-GHz band: 27.5–29.5GHz 795.3.3 32-GHz band: 31.8–33.4GHz 795.3.4 40-GHz band: 37–43.5GHz 795.3.4.1 40-GHz lower band 805.3.4.2 40-GHz upper band 805.3.5 64–71-GHz band 805.4 Spectrum Sharing over mmWave Bands 805.4.1 Factors Determining Sharing vs No Sharing 805.4.1.1 Directionality 815.4.1.2 Deployment and Blockage Density 815.4.1.3 Traffic Characteristics 825.4.1.4 Amount of Sharing 825.4.1.5 Inter-operator Coordination 825.4.1.6 Sharing of Other Resources 835.4.1.7 Multi-user Communication 845.4.1.8 Technical vs Financial Gains 845.5 Spectrum Sharing Options for mmWave Bands 845.5.1 Exclusive Licensing 845.5.2 Unlicensed Spectrum 855.5.2.1 Hybrid Spectrum Access 865.5.3 Spectrum License Sharing 875.5.3.1 Uncoordinated Sharing of Spectrum Licenses 875.5.3.2 Restricted Sharing of Spectrum Licenses 885.5.4 Shared Licenses 905.5.4.1 Spectrum Pooling 905.5.4.2 Partial or Fully Coordinated 905.5.4.3 Common Database 915.5.4.4 Sensing/D2D Communication-based Coordination 915.5.5 Secondary Licenses and Markets 915.5.5.1 Primary/Secondary Markets 925.5.5.2 Third-party Markets 925.5.6 Increasing the utilization of spectrum 925.6 Conclusions 93References 936 The Licensed Shared Access Approach 97António J. Morgado6.1 Introduction to Spectrum Management 976.2 The Dawn of Licensed Shared Access 986.2.1 The LSA Regulatory Environment 996.2.2 LSA/ASA in the 2300–2400 MHz band 1016.3 An Improved LSA Network Architecture 1036.4 Operation of the Improved Architecture in Dynamic LSA Use Cases 1066.4.1 Railway Scenario 1076.4.2 Macro-cellular Scenario 1096.4.3 Small Cell Scenario 1126.5 Summary 115References 1167 Collaborative Sensing Techniques 121Christian Steffens and Marius Pesavento7.1 Sparse Signal Representation 1237.2 Sparse Sensing 1257.3 Collaborative Sparse Sensing 1287.3.1 Coherent Sparse Reconstruction 1297.3.2 Non-Coherent Sparse Reconstruction 1317.4 Estimation Performance 1347.4.1 Comparison of Centralized, Distributed, and Collaborative Sensing 1347.4.2 Source Localization 1367.5 Concluding Remarks 138References 1398 Cooperative Communication Techniques for Spectrum Sharing 147Faheem Khan, Miltiades C. Filippou, and Mathini Sellathurai8.1 Introduction 1478.2 Distributed Precoding Exploiting Commonly Available Statistical CSIT for Efficient Coordination 1498.2.1 Problem Formulation 1508.2.2 Distributed Statistically Coordinated Precoding 1518.2.3 Performance Evaluation 1538.3 A Statistical Channel and Primary Traffic-aware Cooperation Framework for Optimal Service Coexistence 1558.3.1 Joint Design of Spectrum Sensing and Reception for a SIMO Hybrid CR System 1568.3.1.1 Problem Formulation and Solution Framework 1588.3.1.2 Performance Evaluation 1598.3.2 Throughput Performance of Sensing-optimized Hybrid MIMO CR Systems 1618.3.2.1 Problem Formulation and Solution Framework 1618.3.2.2 Performance Evaluation 1628.4 Summary 164References 1659 Reciprocity-Based Beamforming Techniques for Spectrum Sharing in MIMO Networks 169Kalyana Gopala and Dirk T.M. Slock9.1 Multi-antenna Cognitive Radio Paradigms 1699.1.1 Spatial Overlay: MISO/MIMO Interference Channel 1709.1.2 Spatial Underlay 1709.1.3 Spatial Interweave 1709.2 From Multi-antenna Underlay to LSA Coordinated Beamforming 1719.2.1 CoBF and CSIT Discussion 1719.2.2 Some LoS Results 1739.2.3 Noncoherent Multi-user MIMO Communications using Covariance CSIT 1749.3 TDD Reciprocity Calibration 1759.3.1 Fundamentals 1759.3.2 Diagonality of the Calibration Matrix 1789.3.3 Coherent and Non-coherent Calibration Scheme 1789.3.4 UE-aided vs Internal Calibration 1799.3.5 Group Calibration System Model 1799.3.6 Least-squares Solution 1819.3.7 A Bilinear Model 1819.4 MIMO IBC Beamformer Design 1829.4.1 System Model 1829.4.2 WSR Optimization via WSMSE 1829.4.3 Naive UL/DL Duality-based Beamformer Exploiting Reciprocity 1839.5 Experimental Validation 1849.6 Conclusions 188References 18810 Spectrum Sharing with Full Duplex 191Sudip Biswas, Ali Cagatay Cirik, Miltiades C. Filippou, and Tharmalingam Ratnarajah10.1 Introduction 19110.2 Transceiver Design for an FD MIMO CR Cellular Network 19210.2.1 System Model 19210.2.1.1 Signal and Channel Model 19210.2.1.2 SI Cancellation 19410.2.1.3 MSE of the Received Data Stream 19510.2.2 Joint Transceiver Design 19610.2.3 Imperfect CSI and Robust Design 19710.2.3.1 CSI Acquisition 19710.2.3.2 CSI Modeling 19810.2.3.3 Robust Transceiver Design 19810.2.4 Numerical Results 20010.3 Transceiver Design for an FD MIMO IoT Network 20310.3.1 System Model 20410.3.1.1 Signal and Channel Model 20410.3.1.2 SI Cancellation 20510.3.1.3 MSE of the Received Data Stream 20610.3.2 Joint Transceiver Design 20610.3.3 Imperfect CSI and Robust Design 20710.3.4 Numerical Results 20810.4 Summary 209References 210Appendix for Chapter 10 21110.A.1 Useful lemmas 21111 Communication and Radar Systems: Spectral Coexistence and Beyond 213Fan Liu and Christos Masouros11.1 Background and Applications 21311.1.1 Civilian Applications 21311.1.2 Military Applications 21411.2 Radar Basics 21411.3 Radar Communication Coexistence 21611.3.1 Opportunistic Access 21611.3.2 Precoding Designs 21611.3.2.1 Interfering Channel Estimation 21611.3.2.2 Closed-form Precoding 21811.3.2.3 Optimization-based Precoding 21911.4 Dual-functional Radar Communication Systems 22111.4.1 Temporal and Spectral Processing 22111.4.2 Spatial Processing 22211.5 Summary and Open Problems 225References 22612 The Role of Antenna Arrays in Spectrum Sharing 229Constantinos B. Papadias, Konstantinos Ntougias, and Georgios K. Papageorgiou12.1 Introduction 22912.2 Spectrum Sharing 22912.2.1 Spectrum Sharing from a Physical Viewpoint 22912.2.2 Spectrum Sharing from a Regulatory Viewpoint 23112.3 Attributes of Antenna Arrays 23312.4 Impact of Arrays on Spectrum Sharing 23412.4.1 Spectrum Sensing 23412.4.2 Shared Spectrum Access 23412.5 Antenna-Array-Aided Spectrum Sharing 23512.5.1 System Setup 23512.5.2 Assumptions 23612.5.3 System Model 23712.5.3.1 Secondary System 23712.5.3.2 Primary System 23812.5.4 Problem Formulation 23812.5.4.1 Sum-SE, SE, and SINR 23812.5.4.2 Transmission Constraints 23912.5.4.3 Original Optimization Problem 23912.5.4.4 Relaxed Optimization Problem 24012.5.5 Solution and Algorithm 24212.5.5.1 Solution for Other Linear Precoding Schemes 24212.5.6 Performance Evaluation via Numerical Simulations 24312.6 Antenna-Array-Aided Spectrum Sensing 24512.6.1 Printed Yagi–Uda Arrays and Hex-Antenna Nodes 24612.6.2 Test Setup 24812.6.3 Collaborative Spectrum Sensing Techniques 24912.6.4 Experimental Results 25012.6.4.1 Detection in High SNR 25312.6.4.2 Detection in Low SNR 25312.7 Summary and Conclusions 253Acknowledgments 253References 25413 Resource Allocation for Shared Spectrum Networks 257Eduard A. Jorswieck and M. Majid Butt13.1 Introduction 25713.2 Information-theoretic Background 25913.3 Types of Spectrum Sharing 26113.4 Resource Allocation for Efficient Spectrum Sharing 26313.4.1 Multi-objective Programming 26313.4.2 Resource Allocation Games 26513.4.3 Resource Matching for Spectrum Sharing 26713.5 Resource and Spectrum Trading 27013.6 Conclusions and Future Work 275References 27514 Unlicensed Spectrum Access in 3GPP 279Daniela Laselva, David López Pérez, Mika Rinne, Tero Henttonen, Claudio Rosa, Markku Kuusela14.1 Introduction 27914.2 LTE-WLAN Aggregation at the PDCP Layer 28014.2.1 User Plane Radio Protocol Architecture 28114.2.2 Bearer Type and Aggregation 28214.2.3 Flow Control Schemes 28314.3 LTE-WLAN Integration at IP Layer 28414.3.1 User Plane Radio Protocol Architecture 28414.3.2 Flow Control Schemes 28614.4 LTE in Unlicensed Band 28714.4.1 Spectrum and Regulations 28714.4.2 Channel Access 28814.4.3 Frame Structure 28914.4.4 Discovery Reference Signal and RRM 29014.4.5 Uplink Enhancements 29114.5 Performance Evaluation 29414.5.1 Aggregation Gains of LWA and LWIP 29414.5.2 Performance Advantages of LAA 29814.6 Future Technologies 30114.6.1 5G New Radio in Unlicensed Band 30114.6.2 The Role of WLAN in the 5G System 30214.7 Conclusions 302References 30315 Performance Analysis of Spatial Spectrum Reuse in Ultradense Networks 305Youjia Chen, Ming Ding, and David López-Pérez15.1 Introduction 30515.2 Network Scenario and System Model 30615.2.1 Network Scenario 30615.2.2 Wireless System Model 30715.3 Performance Analysis of Full Spectrum Reuse Network 30815.3.1 The Coverage Probability 30815.3.2 The Area Spectral Efficiency 31115.4 Performance with Multi-channel Spectrum Reuse 31215.5 Simulation and Discussion 31215.5.1 Performance with Full Spectrum Reuse Strategy 31315.5.2 Performance with Multi-channel Spectrum Reuse Strategy 31415.6 Conclusion 316Appendix for Chapter 15 31615.A.1 Proof of Lemma 15.1 31615.A.2 Proof of Lemma 15.2 31715.A.3 Proof of Theorem 15.1 318References 31816 Large-scale Wireless Spectrum Monitoring: Challenges and Solutions based on Machine Learning 321Sreeraj Rajendran and Sofie Pollin16.1 Challenges 32116.2 Crowdsourcing 32316.3 Wireless Spectrum Analysis 32416.3.1 Anomaly Detection 32416.3.2 Performance Comparisons 32816.3.3 Wireless Signal Classification 33116.3.3.1 Fully Supervised Models 33116.3.3.2 Semi-supervised Models 33216.3.3.3 Performance-friendly Models 33316.4 Future Research Directions 33516.4.1 Machine Learning 33616.4.2 Anomaly Geo-localization 33616.4.3 Crowd Engagement and Sustainability 33616.5 Conclusion 337References 33717 Policy Enforcement in Dynamic Spectrum Sharing 341Jung-Min (Jerry) Park, Vireshwar Kumar, and Taiwo Oyedare17.1 Introduction 34117.2 Technical Background 34217.3 Security and Privacy Threats 34317.3.1 Sensing-driven Spectrum Sharing 34317.3.1.1 PHY-layer Threats 34417.3.1.2 MAC-layer Threats 34417.3.1.3 Cross-layer Threats 34517.3.2 Database-driven Spectrum Sharing 34517.3.2.1 PHY-layer Threats 34617.3.2.2 Threats to the Database Access Protocol 34617.3.2.3 Threats to the Privacy of Users 34617.4 Enforcement Approaches 34717.4.1 Ex Ante (Preventive) Approaches 34817.4.1.1 Device Hardening 34817.4.1.2 Network Hardening 35017.4.1.3 Privacy Preservation 35117.4.2 Ex Post (Punitive) Approaches 35217.4.2.1 Spectrum Monitoring 35217.4.2.2 Spectrum Forensics 35217.4.2.3 Localization 35317.4.2.4 Punishment 35317.5 Open Problems 35417.5.1 Research Challenges 35417.5.2 Regulatory Challenges 35417.6 Summary 355References 35518 Economics of Spectrum Sharing, Valuation, and Secondary Markets 361William Lehr18.1 Introduction 36118.2 Spectrum Scarcity, Regulation, and Market Trends 36318.3 Estimating Spectrum Values 37018.4 Growing Demand for Spectrum 37318.5 5G Future and Spectrum Economics 37518.6 Secondary Markets and Sharing 38118.7 Conclusion 384References 38519 The Future Outlook for Spectrum Sharing 389Richard Womersley19.1 Introduction 38919.2 Share and Share Alike 39019.3 Regulators Recognize the Value of Shared Access 39319.4 The True Demand for Spectrum 39519.5 The Impact of Sharing on Spectrum Demand 39719.6 General Authorization needed to Encourage Sharing 39919.7 The Long-term Outlook for Spectrum Sharing 401References 403Index 405