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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
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.
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Table of Contents
Cover
About the Editors
List of Contributors
Preface
Abbreviations
1 Introduction: From Cognitive Radio to Modern Spectrum Sharing
1.1 A Brief History of Spectrum Sharing
1.2 Background
1.3 Book overview
1.4 Summary
2 Regulation and Standardization Activities Related to Spectrum Sharing
2.1 Introduction
2.2 Standardization
2.3 Regulation
References
3 White Spaces and Database-assisted Spectrum Sharing
3.1 Introduction
3.2 Demand for Spectrum Outstrips Supply
3.3 Three-tier Access Model
3.4 What is Efficient Use of Spectrum?
3.5 Tapping Unused Capacity: the Evolution of Spectrum Sharing
3.6 Determining which Frequencies are Available to Share: Technology
3.7 Implementing Flexible Spectrum Access
3.8 Foundations for More Flexible Access in the Future
References
Further Reading
4 Evolving Spectrum Sharing Methods, Standards and Trials: TVWS, CBRS, MulteFire and More
4.1 Introduction
4.2 TV White Space
4.3 Emerging Shared Spectrum Technologies
4.4 Conclusion
References
5 Spectrum Above Radio Bands
5.1 Introduction and Motivation for mmWave
5.2 mmWave Communication: What is Different?
5.3 Bands in Above-6 GHz Spectrum
5.4 Spectrum Sharing over mmWave Bands
5.5 Spectrum Sharing Options for mmWave Bands
5.6 Conclusions
References
6 The Licensed Shared Access Approach
6.1 Introduction to Spectrum Management
6.2 The Dawn of Licensed Shared Access
6.3 An Improved LSA Network Architecture
6.4 Operation of the Improved Architecture in Dynamic LSA Use Cases
6.5 Summary
References
7 Collaborative Sensing Techniques
7.1 Sparse Signal Representation
7.2 Sparse Sensing
7.3 Collaborative Sparse Sensing
7.4 Estimation Performance
7.5 Concluding Remarks
References
8 Cooperative Communication Techniques for Spectrum Sharing
8.1 Introduction
8.2 Distributed Precoding Exploiting Commonly Available Statistical CSIT for Efficient Coordination
8.3 A Statistical Channel and Primary Traffic-aware Cooperation Framework for Optimal Service Coexistence
8.4 Summary
References
9 Reciprocity-Based Beamforming Techniques for Spectrum Sharing in MIMO Networks
9.1 Multi-antenna Cognitive Radio Paradigms
9.2 From Multi-antenna Underlay to LSA Coordinated Beamforming
9.3 TDD Reciprocity Calibration
9.4 MIMO IBC Beamformer Design
9.5 Experimental Validation
9.6 Conclusions
References
10 Spectrum Sharing with Full Duplex
10.1 Introduction
10.2 Transceiver Design for an FD MIMO CR Cellular Network
10.3 Transceiver Design for an FD MIMO IoT Network
10.4 Summary
References
Appendix for Chapter 10
11 Communication and Radar Systems: Spectral Coexistence and Beyond
11.1 Background and Applications
11.2 Radar Basics
11.3 Radar Communication Coexistence
11.4 Dual-functional Radar Communication Systems
11.5 Summary and Open Problems
References
12 The Role of Antenna Arrays in Spectrum Sharing
12.1 Introduction
12.2 Spectrum Sharing
12.3 Attributes of Antenna Arrays
12.4 Impact of Arrays on Spectrum Sharing
12.5 Antenna-Array-Aided Spectrum Sharing
12.6 Antenna-Array-Aided Spectrum Sensing
12.7 Summary and Conclusions
Acknowledgments
References
13 Resource Allocation for Shared Spectrum Networks
13.1 Introduction
13.2 Information-theoretic Background
13.3 Types of Spectrum Sharing
13.4 Resource Allocation for Efficient Spectrum Sharing
13.5 Resource and Spectrum Trading
13.6 Conclusions and Future Work
References
14 Unlicensed Spectrum Access in 3GPP
14.1 Introduction
14.2 LTE-WLAN Aggregation at the PDCP Layer
14.3 LTE-WLAN Integration at IP Layer
14.4 LTE in Unlicensed Band
14.5 Performance Evaluation
14.6 Future Technologies
14.7 Conclusions
References
15 Performance Analysis of Spatial Spectrum Reuse in Ultradense Networks
15.1 Introduction
15.2 Network Scenario and System Model
15.3 Performance Analysis of Full Spectrum Reuse Network
15.4 Performance with Multi-channel Spectrum Reuse
15.5 Simulation and Discussion
15.6 Conclusion
Appendix for Chapter 15
References
16 Large-scale Wireless Spectrum Monitoring: Challenges and Solutions based on Machine Learning
16.1 Challenges
16.2 Crowdsourcing
16.3 Wireless Spectrum Analysis
16.4 Future Research Directions
16.5 Conclusion
References
17 Policy Enforcement in Dynamic Spectrum Sharing
17.1 Introduction
17.2 Technical Background
17.3 Security and Privacy Threats
17.4 Enforcement Approaches
17.5 Open Problems
17.6 Summary
References
18 Economics of Spectrum Sharing, Valuation, and Secondary Markets
18.1 Introduction
18.2 Spectrum Scarcity, Regulation, and Market Trends
18.3 Estimating Spectrum Values
18.4 Growing Demand for Spectrum
18.5 5G Future and Spectrum Economics
18.6 Secondary Markets and Sharing
18.7 Conclusion
References
19 The Future Outlook for Spectrum Sharing
19.1 Introduction
19.2 Share and Share Alike
19.3 Regulators Recognize the Value of Shared Access
19.4 The True Demand for Spectrum
19.5 The Impact of Sharing on Spectrum Demand
19.6 General Authorization needed to Encourage Sharing
19.7 The Long-term Outlook for Spectrum Sharing
References
Index
End User License Agreement
List of Tables
Chapter 6
Table 6.1 LSA allocations in railway scenario
Table 6.2 LSA allocations in macro-cellular scenario
Table 6.3 LSA allocations in small cell scenario
Chapter 10
Table 10.1 Sum-MSE minimization algorithm for FD cellular CRN.
Table 10.2 Simulation parameters.
Chapter 12
Table 12.1 Channel notation [42]
Chapter 13
Table 13.1 Many-to-one matching statistics for
and
in percentages: left-ha...
Chapter 16
Table 16.1 Synthetic signal dataset parameters
Table 16.2 Synthetic anomaly dataset parameters
Chapter 17
Table 17.1 Security features compromised by threats.
Chapter 19
Table 19.1 Range of harmonized frequency arrangements in the 2.6 GHz mobile b...
Table 19.2 Different methods for authorizing access to the radio spectrum
List of Illustrations
Chapter 2
Figure 2.1 A high-level comparison of LSA, eLSA, and CBRS.
Figure 2.2 LSA architecture reference model.
Figure 2.3 Mapping of high-level functions and function groups to logical el...
Figure 2.4 CBRS system and interfaces.
Figure 2.5 The FCC's CBRS architecture.
Figure 2.6 3.5 GHz emission mask.
Chapter 3
Figure 3.1 An illustration of white space.
Figure 3.2 Spectrum usage pyramid: primary, secondary, and tertiary.
Figure 3.3 On the margins of a protected service.
Figure 3.4 Hidden node: solo versus cooperative sensing.
Figure 3.5 Spectrum database service architecture.
Figure 3.6 Spectrum data validity.
Chapter 4
Figure 4.1 Network diagram for the Orkney nomadic TVWS installation. The PoP...
Figure 4.2 CBRS functional architecture [10] (modified).
Figure 4.3 MulteFire architecture.
Chapter 5
Figure 5.1 Two distinguishing features of mmWave frequencies. (a) mmWave com...
Figure 5.2 Potential spectrum sharing options for mmWave bands.
Figure 5.3 Comparison of a hybrid spectrum access scheme with a fully licens...
Figure 5.4 Per-user rate achieved by uncoordinated spectrum sharing vs exclu...
Figure 5.5 Comparison of trade-off between edge and peak user-rate achieved ...
Figure 5.6 Impact of coordination and optimal association on spectrum sharin...
Chapter 6
Figure 6.1 Roadmap of LSA in the 2300–2400 MHz band in Europe.
Figure 6.2 Baseline LSA architecture [7].
Figure 6.3 (a) Block-edge mask for synchronized time division LTE base stati...
Figure 6.4 Architecture of a baseline LSA/ASA system operating at 2300–2400 ...
Figure 6.5 System architecture to support the deployment of LSA in dynamic s...
Figure 6.6 Exchange of messages when an LSA licensee requests spectrum.
Figure 6.7 Indoor-to-outdoor, residential small cells.
Chapter 7
Figure 7.1 (a) Setup with
IUs, one single antenna SD, and one SD with an a...
Figure 7.2 (a) Sampling the field of view of an
antenna array in
grid po...
Figure 7.3 PCA of
antennas partitioned in
subarrays and
IUs.
Figure 7.4 Illustration of the matrix structure in the coherent PCA signal m...
Figure 7.5 Spatial spectrum for different SSR techniques and histogram of th...
Figure 7.6 Direction finding performance of the different SSR techniques for...
Figure 7.7 Localization estimation for non-coherent and coherent processing....
Chapter 8
Figure 8.1 System setup, along with the available CSIT at each transmitter....
Figure 8.2 Average data rate of
versus transmit SNR, when
1.75 bps/Hz.
Figure 8.3 Average data rate of
versus transmit SNR, when
1.75 bps/Hz.
Figure 8.4 Average data rate of
versus
, SNR = 8 dB.
Figure 8.5 The investigated system scenario, along with the available CSIR a...
Figure 8.6 Ergodic rate of
versus primary user activity profile when
.
Figure 8.7 Ergodic rate of
versus primary user activity profile (optimized...
Figure 8.8 Ergodic secondary user rate versus primary user outage probabilit...
Figure 8.9 Ergodic secondary user rate versus primary user outage probabilit...
Chapter 9
Figure 9.1 Traditional underlay cognitive radio systems (left) vs. coordinat...
Figure 9.2 EWSR vs SNR for MU MIMO with four Tx and Rx antennas and two user...
Figure 9.3 Pathwise multi-user heterogeneous network scenario.
Figure 9.4 Reciprocity model in TDD.
Figure 9.5 Illustration of coherent and non-coherent calibration.
Figure 9.6 Illustration of the group calibration system model.
Figure 9.7 Illustration of a MIMO IBC scenario.
Figure 9.8 Eurecom MaMIMO prototype and demo set up.
Figure 9.9 Performance of MRT with and without calibration for a 23-antenna ...
Figure 9.10 Performance of MRT and ZF beamformers compared to no beamforming...
Figure 9.11 Comparison of the performance of a naive LMMSE beamformer with t...
Chapter 10
Figure 10.1 An illustration of an FD multi-user MIMO CR cellular system.
Figure 10.2 Convergence behavior of the proposed algorithm.
Figure 10.3 Probability of interference power from secondary to primary netw...
Figure 10.4 Sum-rate comparison of FD and HD systems with respect to RSI can...
Figure 10.5 Sum-rate comparison of FD and HD systems with respect to the CCI...
Figure 10.6 Sum-rate comparison of FD and HD systems with respect to the cha...
Figure 10.7 An illustration of an FD MIMO CR IoT network.
Figure 10.8 Sum-rate comparison of FD and HD systems with respect to RSI can...
Figure 10.9 Sum-rate comparison of FD and HD systems with respect to channel...
Chapter 11
Figure 11.1 Basic operations for a pulsed radar.
Figure 11.2 “Search and track” MIMO radar coexists with the BS.
Figure 11.3 MIMO radar coexists with MU-MIMO downlink.
Figure 11.4 Performance tradeoff between radar and communication systems.
Figure 11.5 MIMO dual-functional radar communication system.
Figure 11.6 Performance of the designed DFRC waveforms.
Chapter 12
Figure 12.1 System setup, notation, and types of interference.
Figure 12.2 Simulations results: standard WF-PA in stand-alone cellular netw...
Figure 12.3 Printed Yagi–Uda antenna array, HA node, and RF switch. (a) Prot...
Figure 12.4 Test setup.
Figure 12.5 Connectivity graph for the 12 sectors of the two HA nodes.
Figure 12.6 Spectrum sensing results in the high SNR regime.
Figure 12.7 Spectrum sensing results in the low SNR regime.
Chapter 13
Figure 13.1 System model for the two-user interference channel.
Figure 13.2 Achievable rate region of the two-user two-carrier IFC with TIN ...
Figure 13.3 Classification of spectrum sharing methods: (a) no spectrum shar...
Figure 13.4 Achievable rate region of the two-user two-carrier IFC and two o...
Figure 13.5 Achievable rate region for the two-user two-carrier interference...
Figure 13.6 Two-sided one-to-many matching market model with
users (colleg...
Figure 13.7 PAL holders will be allowed to lease any bandwidth for any perio...
Figure 13.8 Spectrum sharing scenario between two eNBs of two different oper...
Figure 13.9 A practical spectrum trading system: two operators exchange spec...
Figure 13.10 The UPT of a user depending on the duration of a contract
com...
Figure 13.11 Example of user arrival and buffer processes for the baseline s...
Chapter 14
Figure 14.1 LWA user-plane radio protocol architecture for the scenarios whe...
Figure 14.2 LWIP user-plane radio protocol architecture for the scenarios wh...
Figure 14.3 5-GHz unlicensed band availability by region.
Figure 14.4 Cat4 LBT procedure standardized by the 3GPP.
Figure 14.5 LAA frame structure, including downlink burst and uplink burst w...
Figure 14.6 LAA DRS structure and DMTC for RRM measurements.
Figure 14.7 Schematic of B-IFDMA.
Figure 14.8 LAA uplink (UL) scheduling framework.
Figure 14.9 Enterprise layout for LWA/LWIP evaluation.
Figure 14.10 Downlink session throughput of LTE-only and WLAN-only reference...
Figure 14.11 System throughput aggregated across all the UEs in the network ...
Figure 14.12 Indoor scenario for capacity evaluation.
Figure 14.13 Mean UE session throughput comparison between LAA and WLAN, one...
Figure 14.14 Mean UE session throughput comparison between LAA and WLAN, two...
Figure 14.15 Mean UE session throughput comparison between LAA and WLAN, mix...
Figure 14.16 Coverage comparison between LAA and WLAN.
Chapter 15
Figure 15.1 The coverage probability
versus
for the 3GPP case with
and...
Figure 15.2 The ASE
versus
for the 3GPP case with
,
, and various valu...
Figure 15.3 The coverage probability
versus
with
and various values of...
Figure 15.4 The ASE
versus
with
and various values of
and
.
Chapter 16
Figure 16.1 High-level overview of the Electrosense network. Low-cost sensor...
Figure 16.2 Model architecture for anomaly detection.
Figure 16.3 Sample signals
single-cont
,
single-rshort
,
mult-cont
, and
dethop
Figure 16.4 Anomaly detection accuracies for different anomalies with a cons...
Figure 16.5 ROC curves for different detection algorithms on
det-hop
synthet...
Figure 16.6 Detected anomalies for a duration of 500 hours from one of the E...
Figure 16.7 Wireless classification accuracy of CNN and LSTM deep learning m...
Figure 16.8 Generator and discriminator details.
Figure 16.9 Classification results for a fading channel with receiver effect...
Figure 16.10 Wireless signal classification accuracy of two layer quantized ...
Chapter 17
Figure 17.1 Taxonomy of threats to spectrum sharing.
Figure 17.2 Primary user emulation attack.
Figure 17.3 Spectrum sensing data falsification attack.
Figure 17.4 Taxonomy of enforcement approaches for spectrum sharing.
Figure 17.5 Components of an ontology-based policy reasoner.
Figure 17.6 Trade-off between location privacy of the PU and spectrum utiliz...
Chapter 18
Figure 18.1 Global trends in spectrum prices, by band and auction, 2000–2016...
Chapter 19
Figure 19.1 Extract from the ITU Radio Regulations showing mobile allocation...
Figure 19.2 Different forecasts for mobile data traffic growth.
Figure 19.3 Spectrum requirements for IMT-2020 based on a range of use cases...
Guide
Cover
Table of Contents
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Spectrum Sharing
The Next Frontier in Wireless Networks
Edited by
Constantinos B. Papadias
The American College of Greece
Athens
Greece
Tharmalingam Ratnarajah
University of Edinburgh
Edinburgh
UK
Dirk T.M. Slock
EURECOM
Sophia Antipolis
France
This edition first published 2020
© 2020 John Wiley & Sons Ltd
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The right of Constantinos B. Papadias, Tharmalingam Ratnarajah and Dirk T.M. Slock to be identified as the authors of the editorial material in this work has been asserted in accordance with law.
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Library of Congress Cataloging-in-Publication Data applied for
Hardback ISBN: 9781119551492
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For Maria-Anna, Anna, Billy and Dimitri, C.B.P.
For the memory of my father, Dr. D. Tharmalingam and brother, D. Varatharajah, T.R.
For Aida, my parents, our families, and my students, D.T.M.S.
About the Editors
Constantinos B. Papadias is the Executive Director of the Research, Technology and Innovation Network (RTIN) of The American College of Greece, where he is also a faculty member, since Feb. 1, 2020. Prior to that, he was the Scientific Director / Dean of Athens Information Technology (AIT), in Athens, Greece, where he was also Head of the Broadband Wireless and Sensor Networks (B-WiSE) Research Group. He is currently an Adjunct Professor at Aalborg University and at the University of Cyprus. He received the Diploma of Electrical Engineering from the National Technical University of Athens (NTUA) in 1991 and the Doctorate degree in Signal Processing (highest honors) from the Ecole Nationale Supérieure des Télécommunications (ENST), Paris, France, in 1995. He was a researcher at Institut Eurécom (1992–1995), Stanford University (1995–1997) and Lucent Bell Labs (as Member of Technical Staff from 1997–2001 and as Technical Manager from 2001–2006). He was Adjunct Professor at Columbia University (2004–2005) and Carnegie Mellon University (2006–2011). He has published over 200 papers and 4 books and has received over 9000 citations for his work, with an h-index of 43. He has also made standards contributions and holds 12 patents. He was a member of the Steering Board of the Wireless World Research Forum (WWRF) from 2002–2006, a member and industrial liaison of the IEEE's Signal Processing for Communications Technical Committee from 2003–2008 and a National Representative of Greece to the European Research Council's IDEAS program from 2007–2008. He has served as member of the IEEE Communications Society's Fellow Evaluation and Awards Committees, as well as an Associate Editor for various journals. He has contributed to the organization of several conferences, including, as General Chair, the IEEE CTW 2016 and the IEEE SPAWC 2018 workshops. He has acted as Technical Coordinator in several EU projects such as: CROWN in the area of cognitive radio; HIATUS in the area of interference alignment; HARP in the area of remote radio heads and ADEL in the area of licensed shared access. He is currently the Research Coordinator of the European Training Network project PAINLESS on the topic of energy autonomous infrastructure-less wireless networks as well as the Technical Coordinator of the EU CHIST-ERA project FIREMAN on the topic of predictive maintenance via machine learning empowered wireless communication networks. His distinctions include the Bell Labs President's Award (2002), the IEEE Signal Processing Society's Young Author Best Paper Award (2003), a Bell Labs Teamwork Award (2004), his recognition as a “Highly Cited Greek Scientist” (2011), two IEEE conference paper awards (2013, 2014) and a “Best Booth” Award at EUCNC (2016). He was a Distinguished Lecturer of the IEEE Communications Society for 2012–2013. He was appointed Fellow of IEEE in 2013 and Fellow of the European Alliance of Innovation (EAI) in 2019.
Tharmalingam Ratnarajah is currently with the Institute for Digital Communications, the University of Edinburgh, Edinburgh, UK, as a Professor in Digital Communications and Signal Processing. He was a Head of the Institute for Digital Communications during 2016–2018. Prior to this, he was with McMaster University, Hamilton, Canada, (1997–1998), Nortel Networks (1998–2002), Ottawa, Canada, University of Ottawa, Canada, (2002–2004), Queen's University of Belfast, UK, (2004–2012). His research interests include signal processing and information theoretic aspects of 5G and beyond wireless networks, full-duplex radio, mmWave communications, random matrices theory, interference alignment, statistical and array signal processing and quantum information theory. He has published over 400 publications in these areas and holds four U.S. patents. He has supervised 15 PhD students and 20 post-doctoral research fellows, and raised $11 million+ USD of research funding. He was the coordinator of the EU projects ADEL in the area of licensed shared access for 5G wireless networks, HARP in the area of highly distributed MIMO, as well as EU Future and Emerging Technologies projects HIATUS in the area of interference alignment and CROWN in the area of cognitive radio networks. Dr Ratnarajah was an associate editor IEEE Transactions on Signal Processing, 2015–2017 and Technical co-chair, The 17th IEEE International workshop on Signal Processing advances in Wireless Communications, Edinburgh, UK, 3–6, July, 2016. Dr Ratnarajah is a member of the American Mathematical Society and Information Theory Society and Fellow of Higher Education Academy (FHEA).
Dirk T.M. Slock received an electronics engineering degree from Ghent University, Belgium in 1982. In 1984 he was awarded a Fulbright scholarship for Stanford University, USA, where he received the MSEE, MS in Statistics, and PhD in EE in 1986, 1989 and 1989 resp. While at Stanford, he developed new fast recursive least-squares algorithms for adaptive filtering. In 1989–91, he was a member of the research staff at the Philips Research Laboratory Belgium. In 1991, he joined EURECOM where he is now professor. At EURECOM, he teaches statistical signal processing (SSP) and signal processing techniques for wireless communications. His research interests include SSP for wireless communications (antenna arrays for (semi-blind) equalization/interference cancellation and spatial division multiple access (SDMA), space-time processing and coding, channel estimation, diversity analysis, information-theoretic capacity analysis, relaying, cognitive radio, geolocation), and SSP techniques for audio processing. He invented semi-blind channel estimation, the chip equalizer-correlator receiver used by 3G HSDPA mobile terminals, spatial multiplexing cyclic delay diversity (MIMO-CDD) now part of LTE, and his work led to the Single Antenna Interference Cancellation (SAIC) integrated in the GSM standard in 2006. Recent research keywords are MIMO interference channel, multi-cell, distributed resource allocation, variational and empirical Bayesian techniques, large random matrices, stochastic geometry, audio source separation, location estimation and exploitation.
In 25 years, he has graduated over 35 PhD students, 9 of which are in academia (6 professors), and about 10 others are in research in industry. His research led to: h-index: 41, total citations: 8800, 10 book chapters, 50 journal papers, 500 conference papers. In 1992 he received one best journal paper award from IEEE-SPS and one from EURASIP. He is the coauthor of two IEEE Globecom'98, one IEEE SIU'04, one IEEE SPAWC'05, one IEEE WPNC'16 and one IEEE SPAWC'18 best student paper award, and an honorary mention (finalist in best student paper contest) at IEEE SSP'05, IWAENC'06, IEEE Asilomar'06 and IEEE ICASSP'17. He has been an associate editor for various journals, and conference organizer of SPAWC'06, IWAENC'14, EUSIPCO'15. He was a member of the IEEE-SPS Awards Board 2011–13 and of the EURASIP JWCN Awards Committee. Over the past 10 years he has participated in the French projects ERMITAGES, ANTIPODE, PLATON, SEMAFOR, APOGEE, SESAME, DIONISOS, and DUPLEX (which he coordinated), MASS-START and GEOLOC, summing to over 2M€ in funding, and in the European projects K-SPACE, Newcom/++/#, WHERE(2), CROWN, SACRA, ADEL and HIGHTS summing up to over 2.5M€ in funding. He has also had a number of direct research contracts with Orange (6), Philips, NXP, STEricsson, Infineon, and Intel, and scholarships for 10 PhD students. He cofounded in 2000 SigTone, a start-up developing music signal processing products, and in 2014 Nestwave, a start-up developing Ultra Low-Power Indoor and Outdoor Mobile Positioning. He has also been active as a consultant on xDSL, DVB-T and 3G systems. He is a Fellow of IEEE and EURASIP. In 2018 he received the URSI France medal.
List of Contributors
Dani Anderson
Department of Electronic and Electrical Engineering
University of Strathclyde
Glasgow
United Kingdom
Adrish Banerjee
Department of Electrical Engineering
Indian Institute of Technology Kanpur
Kanpur
India
Sudip Biswas
Indian Institute of Information Technology
Guwahati
India
M. Majid Butt
Nokia Bell Labs
Paris-Saclay
France
Ali Cagatay Cirik
Ofinno Technologies
USA
Youjia Chen
Fuzhou University
Fuzhou
P.R. China
David Crawford
Department of Electronic and Electrical Engineering
University of Strathclyde
Glasgow
United Kingdom
Ming Ding
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Eveleigh
Australia
María Dolores (Lola) Pérez Guirao
Sennheiser Electronic GmbH & Co. KG
Wedemark
Germany
Miltiades C. Filippou
Intel Deutschland GmbH
Neubiberg
Germany
Kalyana Gopala
Institut Eurecom
Communication Systems Department
Biot Sophia Antipolis
France
Abhishek K. Gupta
Department of Electrical Engineering
Indian Institute of Technology Kanpur
Kanpur
India
Tero Henttonen
Nokia Bell Labs CTO
Espoo
Finland
Eduard A. Jorswieck
TU Braunschweig
Braunschweig
Germany
Faheem Khan
School of Computing and Engineering
University of Huddersfield
Queensgate
Huddersfield
United Kingdom
Vireshwar Kumar
Virginia Tech
Arlington
USA
Markku Kuusela
Nokia CSD Digital Automation
Lahti
Finland
Daniela Laselva
Nokia Bell Labs
Aalborg
Denmark
William Lehr
Massachussetts Institute of Technology
Cambridge
USA
Fan Liu
Department of Electronic & Electrical Engineering
University College London
London
United Kingdom
David Lópéz-Pérez
Nokia Bell Labs
Dublin
Ireland
Christos Masouros
Department of Electronic & Electrical Engineering
University College London
London
United Kingdom
António J. Morgado
Instituto de Telecomunicações
Aveiro
Portugal
Markus Mueck
Intel Deutschland GmbH
Neubiberg
Germany
Konstantinos Ntougias
University of Cyprus
Nicosia
Cyprus
Taiwo Oyedare
Virginia Tech
Arlington
USA
Constantinos B. Papadias
Research, Technology and Innovation Network
The American College of Greece
Athens
Greece
Georgios K. Papageorgiou
Heriot-Watt University
Edinburgh
United Kingdom
Jung-Min (Jerry) Park
Virginia Tech
Arlington
USA
David Lópéz-Pérez
Nokia Bell Labs
Dublin
Ireland
Marius Pesavento
Darmstadt University of Technology
Darmstadt
Germany
Sofie Pollin
KU Leuven
Heverlee
Belgium
Sreeraj Rajendran
KU Leuven
Heverlee
Belgium
Rao Yallapragada
Intel Corp.
San Diego
USA
Tharmalingam Ratnarajah
University of Edinburgh
Edinburgh
United Kingdom
Mika Rinne
Nokia Technologies
Espoo
Finland
Claudio Rosa
Nokia Bell Labs
Randers
Denmark
Mathini Sellathurai
School of Engineering & Physical Sciences
Heriot-Watt University
Edinburgh
United Kingdom
K.A. Shruthi
Department of Electronic and Electrical Engineering
University of Strathclyde
Glasgow
United Kingdom
Dirk T.M. Slock
EURECOM
Communication Systems Department
Biot Sophia Antipolis
France
Srikathyayani Srikanteswara
Intel Corp.
OR
USA
Christian Steffens
Hyundai Mobis
Frankfurt
Germany
Robert W. Stewart
Department of Electronic and Electrical Engineering
University of Strathclyde
Glasgow
United Kingdom
Andrew Stirling
Larkhill Consultancy
Surrey
United Kingdom
Richard Womersley
LS Telcom
Germany
Preface
Our efforts over the years to tame the air as a communication medium have been hampered by the electromagnetic spectrum's limiting nature since the early days of radio. Unlike wired communication over, for example, copper wires or fiber, where new channels can be added simply by using more cables, wireless communication systems and networks have always had to struggle to fit as many communication links as possible into a given geographic area through the same medium. Given the finite available spectrum (due to nature, regulation and to the transmitter and receivers' capabilities) and Shannon's fundamental law of channel capacity, electromagnetic spectrum management has become a crucial ongoing need that accompanys all types and generations of wireless systems and networks.
The canonical paradigm in spectrum allocation has been to provide orthogonal channels to the different users in a given geographic area – and then of course to reuse the same spectrum in other geographic areas. This simple principle, including a careful frequency planning and dimensioning of the resulting interference, has allowed cellular networks to develop rapidly since the late 1980s all the way to today's phenomenal success of 4G and emerging 5G networks, which have impacted all types of human activity and have changed the way we interact, do business, and provide various services to citizens. In order to meet the cellular networks' growing demands in data rates, capacity, and quality-of-service (QoS) requirements, more and more spectrum keeps being allocated, typically through government-based licensing that provides exclusive (often national level) rights of use to a number of operators, usually for a high fee, following the orthogonal allocation paradigm mentioned earlier. The orthogonal model has permitted operators to provide QoS guarantees to their users.
However, in parallel with the strict paid licensing model mentioned above, unlicensed use of the spectrum has been also allowed for a number of applications that do not need to provide QoS guarantees to their users and whose range and user density are smaller than that of cellular networks. Such applications included, in the early years, amateur radio, cordless phones, and even non-communication uses such as microwave ovens and other appliances. A big boost to the unlicensed use of spectrum was undoubtedly given by the proliferation of wireless local area networks (LANs) that rely on Wi-Fi-type systems. In spite of the lack of QoS guarantees (and benefiting from continuously improved protocols), Wi-Fi has become a huge success, largely due to its fee-free use and little interference in several, typically static, environments (such as the home or the office). As a result, these networks carry an amount of wireless data that is comparable to that of their cellular counterparts.
In parallel with the above core models of spectrum usage (licensed and unlicensed), a third paradigm has emerged over the last two decades, wherein unlicensed operators would make use of licensed spectrum. This concept originated with the advent of cognitive radio and has gone through various phases since. It relies on the key requirement that the operator who does not hold a license should not interfere with the ones who do. This may be easier in cases of sparse usage as well as when the licensed spectrum is largely unused, but is much more challenging in dense usage and crowded spectrum situations; hence, in order to succeed, this model requires a very good awareness of the spectrum activity in a given area (attained via either spectrum sensing or geolocation databases, or both), as well as of course a careful design of the wireless communication protocol used.
Collectively called “spectrum sharing,” these techniques are gaining increased traction and have evolved significantly over the last decade. This is largely due to the continued (exponential-like) growth of wireless service demands, the “addiction” of users to unlicensed broadband access, the saturation of existing licensed spectrum usage in many areas, the emergence of new types of operators and service models, the proliferation of research activity in spectrally efficient technologies, and the rather slow and bureaucratic nature of spectrum auctioning.
The purpose of this book has been to collect, in a single volume, the key technologies and approaches related to spectrum sharing, dating back to the inception of the cognitive radio concept and going all the way to today's novel approaches and emerging research concepts. Our goal has been to capture all the related dimensions, including the technical, key regulatory, standardization, and financial aspects.
We have been privileged to collaborate in the context of two important collaborative research projects that have received funding from the European Commission (under its 7th Framework Program), whose generous support is herein gratefully acknowledged. These projects are FET Open project CROWN (Cognitive Radio Oriented Wireless Networks) which ran from 2009 to 2012, and Future Networks project ADEL (Advanced Dynamic spectrum 5G mobile networks Employing Licensed shared access), which ran from 2013 to 2016. Key spectrum sharing concepts were introduced in these projects ahead of their time (such as that of horizontal sharing even within the same operator suggested in CROWN, now used in LTE Licensed Assisted Access (LAA), and sensing-assisted Licensed Shared Access proposed in ADEL, now used in the Spectrum Access System (SAS) in the USA). These projects allowed us not only to participate in the fascinating research on spectrum sharing, introducing to it several PhD students and young researchers, but also to stay in touch with the most current trends, interact with all types of stakeholders (from industrial to regulatory to end users), and contribute to exciting proof-of-concept demos of emerging solutions. They also helped us to establish numerous research collaborations with a growing number of research teams that have continued and expanded beyond these projects and due to which this endeavor is largely owed.
Given the spurt of activity in spectrum sharing and our personal involvement and interactions, we felt that the time was right for a comprehensive edited volume on the topic, written by some of the top experts in all related areas. We were highly encouraged by the many positive responses for chapter contributions and are grateful to all the authors for their inputs and for allowing us to cover all the topics that we deemed important, including very recent ones such as full duplex-based spectrum sharing, communication-radar coexistence, mmWave, massive MIMO, and machine learning-based spectrum monitoring, among others.
Our addressable audience includes readers from the academic (students, professors), industrial (engineers, practitioners), as well as regulatory/standardization sectors, who share an interest on how spectrum has been used to date and how it can be best used and shared in the coming years.
To the extent that the interested reader will find the answers they are looking for and acquire a well-rounded knowledge of spectrum sharing technology and its surrounding ecosystem, our goal will have been met. We hope that all readers will do so and that this book becomes a useful item of their library and a reference for years to come!
Constantinos B. Papadias
Athens, Greece
Tharmalingam Ratnarajah
Edinburgh, United Kingdom
Dirk T.M. Slock
Sophia Antipolis, France
Dedicated to the many researchers and engineers whose contributions over the years have made this book possible.
Abbreviations
3D
three-dimensional
3G
third generation
3GPP
3rd Generation Partnership Project
4G
fourth generation
5G
fifth generation
5GS
5G system
AAE
adversarial autoencoder
ADC
analog-to-digital converter
ADEL
advanced dynamic spectrum 5G mobile networks employing licensed shared access
AI
artificial intelligence
AMC
automatic modulation classification
AMPS
advanced mobile phone system
AP
access point
API
application programming interface
APT
Asia Pacific Telecommunity
ASA
authorized shared access
ASE
area spectral efficiency
ATC
air traffic control
AUL
autonomous uplink transmission
AWGN
additive white Gaussian noise
BC
broadcast channel
BF
beacon falsification
BF
beamformer/beamforming
B-IFDMA
block-interleaved frequency division multiple access
BnB
branch-and-bound
BNetzA
German Regulation Administration
BPDN
basis pursuit denoising
BPSK
binary phase shift keying
BS
base station
BSS
basic service set
BWA
broadband wireless access
CAPEX
capital expenditure
Cat2
Category 2 LBT
Cat4
Category 4 LBT
CBF
coordinated beamforming
CBRS
Citizens Broadband Radio Service
CBSD
Citizens Broadband Service device
CCA
clear channel assessment
CCC
control channel corruption
CCD
complementary cumulative distribution
CCI
co-channel interference
CD
code-division multiple access
CEPT
European Conference of Postal and Telecommunication Administration
CFAR
constant false-alarm rate
CI
constructive interference
CITEL
Inter-American Telecommunication Commission
CMC
constant-modulus constraint
CNN
convolutional neural network
CoBF
coordinated beamformer/beamforming
CoMP
coordinated multi-point
COT
channel occupancy time
CPE
customer premise equipment
CR
cognitive radio
C-RAN
Cloud RAN
CRS
common reference signals
CRSS
communication and radar spectrum sharing
CSI
channel state information
CSIR
channel state information at the receiver
CSI-RS
Channel State Information-Reference Signals
CSIT
channel state information at the transmitter
CSMA/CA
carrier sense multiple access with collision avoidance
CU
central unit
CWSC
University of Strathclyde's Centre for White Space Communications
D2D
device-to-device
DAC
digital-to-analog converter
DAPA
database access protocol attack
dB
decibel
DFH
dynamic frequency hopping
DFRC
dual-functional radar communication
DIA
database inference attack
DL
downlink
DMTC
discovery reference signal measurement timing configuration
DoA
direction of arrival
DoD
Department of Defense
DoD
direction of departure
DoF
degree of freedom
DoS
denial of service
DR
dynamic range
DRS
discovery reference signal
DSA
dynamic spectrum access
DSP
digital signal processing
DSS
dynamic spectrum sharing
DSSS
direct-sequence spread spectrum
DTV
digital television
DVB-T
digital video broadcasting — terrestrial
EC
European Commission
ECC
Electronic Communications Committee
EC/CEPT
European Conference of Postal and Telecommunication Administration
ED
energy detection
EIRP
equivalent isotropically radiated power
eLAA
enhanced licensed assisted access
eLSA
evolved licensed shared spectrum
eMBB
enhanced mobile broadband
eNB
evolved node B
EPC
evolved packet core
ESC
environmental sensing capability
ESIP-WSR
expected signal and interference power
ETEB
estimated time to empty buffers
ETSI
European Telecommunications Standards Institute
EU
European Union
EWSMSE
expected weighted sum mean squared error
EWSR
expected (or ergodic) weighted sum rate
FCC
Federal Communications Commission
FCC
first coefficient constraint
FD
full duplex
FDD
frequency-division duplex
FDMA
frequency-division multiple access
FFT
fast Fourier transform
FHSS
frequency hopping spread spectrum
FIS
forward inter-system
FrFT
fractional Fourier transform
FS
frame structure
FSS
fixed satellite services
FSS
fixed satellite system
FTP
file transfer protocol
GAA
general authorized access
GDD
geolocation database dependent
GHz
gigahertz
GNSS
global navigation satellite system
GPS
global positioning system
GRE
generic routing encapsulation
GSM
global system for mobile
HA
hex-antenna
HARQ
hybrid automatic-repeat-request
HD
half duplex
HMM
hidden Markov model
HT
hypothesis testing
IBC
interfering broadcast channel
IA
interference alignment
ICA
independent component analysis
ICD
initial commercial deployment
ICI
inter-cell interference
ICPA
interference-constrained PA
ICSI
interfering channel state information
ICT
information computing and telecommunications
IEEE
Institute of Electrical and Electronics Engineers
IETF
Internet Engineering Task Force
IFC
interference channel
IFFT
inverse fast Fourier transform
i.i.d.
independent and identically distributed
IMT
international mobile telecommunications
InfoGAN
Information Maximizing Generative Adversarial Networks
InH
indoor hotspot
IoT
Internet of Things
IP
Internet protocol
IPC
interference-power constraint
IPSec
IP security
IPT
interference power threshold
IQ
in-phase and quadrature phase
ISM
industrial, scientific, and medical
ISP
Internet service provider
ISS
inter-satellite service
ITM
international mobile telecommunications
ITRSSL
interference threshold restricted sharing of spectrum licenses
ITU
International Telecommunications Union
IU
incumbent user
JRC
Joint Research Center of the European Commission
KKT
Karush–Kuhn–Tucker
KPI
key performance indicator
LAA
licensed assisted access
LAN
local access network
LBT
listen-before-talk
LMDS
local multipoint distribution service
LMI
linear matrix inequality
LMMSE
linear minimum mean squared error
LoS
line-of-sight
LPI
low-probability-of-intercept
LS
least squares
LSA
licensed shared access
LSTM
long short-term memory
LTE
long-term evolution
LTE-A
long-term evolution advanced
LTE-LAA
long-term evolution – licensed assisted access
LTE-U
LTE in unlicensed spectrum
LU
licensee user
LWA
LTE-WLAN (radio) aggregation
LWAAP
LWA adaptation protocol
LWIP
LTE WLAN radio level integration with Internet protocol security tunnel
LWIPEP
LWIP encapsulation protocol
MAC
media access control
MaMIMO
massive multiple input multiple output
MED
maximum-eigenvalue-based detection
MF
matched filter
MFCN
mobile/fixed communications network
MIMO
multiple input multiple output
MISO
multiple input single output
ML
machine learning
MMSE
minimum mean squared error
mmWave
millimeter-wave
MNO
mobile network operator
MOP
multi-objective programming
MRC
maximal ratio combining
MRT
maximum ratio transmission
MS
mobile stations
MSE
mean squared error
MU
multi-user
MUI
multi-user interference
MU-MIMO
multi-user MIMO
MVNO
mobile virtual network operator
NaaS
network as a service
NBS
Nash bargaining solution
NE
Nash equilibrium
NEWSR
naive expected (or ergodic) weighted sum rate
NG-RAN
next generation (5G) radio access network
NHN
neutral host networks
NI
National Instrument
NLoS
non-line-of-sight
NOI
notice of inquiry
NPRM
notice of proposed rulemaking
NR
new radio
NRA
national regulatory agency
NRA
national regulation administration
NRA
national regulatory authority
NR-U
new radio in unlicensed spectrum
NSF
National Science Foundation
NSP
null-space projection
OA&M
operations, administration, and management
OAM&P
operations, administration, management, and provisioning
Ofcom
Office of Communications
OFDM
orthogonal frequency division multiplexing
OFDMA
orthogonal frequency division multiple access
OOB
out of band
OPEX
operating expenditure
ORAN
Open Radio Access Network
OSA
opportunistic spectrum access
OSDaaS
Open Spectrum Data as a Service
OTA
over-the-air
P2MP
point to multi-point
P2P
point-to-point
PA
power amplifier
PA
priority access
PAL
priority access license
PAWS
protocol to access white space
PCA
partly calibrated array
PCS
personal communication service
PCI
physical cell-identity
PDCP
packet data convergence protocol
probability density function
PDU
protocol data unit
PHY
physical
PIM
pulse interval modulation
PMSE
program making and special events
POE
power over Ethernet
PoP
point of presence
PPA
PAL protection area
PPDR
public protection and disaster relief
PPP
Poisson point process
PRB
Physical Resource Block
PRF
pulse repetition frequency
PRI
pulse repetition interval
PS
primary system
PSD
power spectral density
PSS
primary synchronization signal
PSK
phase shift keying
PU
primary user
PUSCH
physical uplink shared channel
QAM
quadrature amplitude modulation
QCQP
quadratically constrained quadratical programming
QoS
quality of service
QPSK
quadrature phase shift keying
R&O
report and order
RA
resource allocation
RadioML
radio machine learning
RAN
radio access network
RAT
radio access technology
RB
resource blocks
RCC
Regional Commonwealth in the Field of Communications
RCS
radar cross-section
RF
radio frequency
RFID
radio frequency identification
RIS
reverse inter-system
RLC
radio link control
RLS
radio location services
RMSE
root mean square error
RRM
radio resource management
RRS
reconfigurable radio systems
RSI
residual self-interference
RSPG
Radio Spectrum Policy Group
RSRP
reference signal received power
RSRQ
reference signal received quality
RSS
received signal strength
RSSI
received signal strength indicator
RSSL
restricted sharing of spectrum licenses
RV
random variable
Rx/RX
receive/receiver/reception
RZF
regularized zero forcing
SAIFE
spectrum anomaly detector with interpretable features
SAS
spectrum access system
SBW
small back-off window
SC
similarity constraint
SCH
superframe control header
SCN
small cell network
SD
sensing device
SDMA
space-division multiple access
SDP
semi-definite programming
SDR
software-defined radio
SDR
semi-definite relaxation
SE
spectral efficiency
SeGW
security gateway
SI
self-interference
SIC
successive interference cancellation
SINR
signal-to-interference-plus-noise ratio
SIR
signal-to-interference ratio
SIMO
single input multiple output
SINR
signal-to-interference-plus-noise ratio
SISO
single input single output
SLNR
signal-to-leakage-plus-noise ratio
SND
simultaneous non-unique decoding
SNR
signal-to-noise ratio
SON
self-organizing network
SP
spectrum provider
SPC
sum-power constraint
SR
sum-rate
SRM
secure radio middleware
SS
spectrum sharing
SS
secondary system
SSC
WInnForum's Spectrum Sharing Committee
SSDF
spectrum sensing data falsification
SSR
spatial spectrum reuse
SSS
secondary synchronization signal
STA
station
SU
secondary user
SU
spectrum user
SULI
spectrum utilization-based location inference
SVD
singular-value decomposition
SVM
support vector machine
sZF
statistical zero-forcing
TC
Technical Committee
TCP
transmission control protocol
TD
time division
TDD
time-division duplex
TDMA
time-division multiple access
TDOA
time difference of arrival
TIN
treating interference as noise
TIP
Telecom Infra Project
TPC
transmission power constraint
TR
Technical Report
TRAI
Telecom Regulatory Authority of India
TVHT
television very high throughput
TVWS
TV white space
Tx/TX
transmit/transmitter/transmission
TxOP
transmission opportunity period
UAS
user associated strategy
UAV
unmanned aerial vehicle
UCI
uplink control information
UDN
ultradense network
UDP
user datagram protocol
UE
user equipment
UHF
ultrahigh frequency
UKPM
UK Prediction Model
UL
uplink
ULA
uniform linear array
UMI
Urban Micro
UPT
user perceived throughput
USRP
universal software radio peripheral
USSL
uncoordinated sharing of spectrum licenses
UWB
ultrawide band
V2X
vehicle-to-everything
VAE
variational autoencoder
VHF
very high frequency
VoIP
voice over IP
WF
water-filling
Wi-Fi
wireless fidelity
WiGig
Wireless Gigabits Alliance
WiMAX
Worldwide Interoperability for Microwave Access
WInnForum
Wireless Innovation Forum
WLAN
wireless local area network
WRAN
wireless regional area network
WRC
World Radiocommunication Conferences
WSD
white space device
WSDB
white space database
WSMSE
weighted sum mean squared error
WSN
wireless sensor networks
WSR
weighted sum rate
WT
WLAN termination
ZF
zero forcing/forced
1Introduction: From Cognitive Radio to Modern Spectrum Sharing
Constantinos B. Papadias1*, Tharmalingam Ratnarajah2, and Dirk T.M. Slock3
1The American College of Greece, Greece
2University of Edinburgh, UK
3Institut Eurecom, France
1.1 A Brief History of Spectrum Sharing
Limited spectrum availability is a real constraint for existing and future wireless systems. Spectrum scarcity is one key factor that prevents operators from meeting the increasing user demands in capacity and quality of service (QoS) and induces additional expenditures (capital expenditure and operating expenditure) that network operators reflect in the service prices to their customers. The introduction of novel spectrum management paradigms can address the spectrum crunch issue. Furthermore, it allows new types of players (operators, also called “users”) who might not otherwise be able to afford or wish to have an exclusive/national-level license to provide service with QoS guarantees to their clients through a substantially smaller investment.
The use of the spectrum in commercial applications is typically either licensed or license-exempt. Spectrum sharing, wherein both licensed and license-exempt (or other types of non-exclusively licensed) users co-exist within the same frequency bands in a given geographic location, first explored via the concept of cognitive radio (CR), is an alternative approach in spectrum usage. CR is traditionally thought of as a technology that enables non-licensed secondary users (SUs) to make use of idle spectrum without causing harmful interference to licensed primary users (PUs). As such, it was regarded with suspicion by mobile broadband operators, who were reluctant to allow the use of their expensively acquired spectrum by any SU that claimed they would respect the regulatory CR policies. This reluctance on the side of legacy operators was accentuated by the fact that, in its original form, CR, which was first considered for the so-called TV white space (TVWS) spectrum freed by former analog TV providers, relied heavily on spectrum sensing in order to avoid causing interference to PUs. This was clearly insufficient due to the low levels of sensing sensitivity, the well-known hidden node problem, etc. The architectural (supported by regulation) addition of using a spectrum registry (database) in order to better/further prevent harmful interference to the PUs improved the situation, but was still insufficient to make CR take off as a service paradigm. Traditional CR was also problematic from the SUs viewpoint, as it could only guarantee a QoS level similar (at best) to unlicensed access, i.e., with no guarantees.
The next important milestone emerged in early 2011, when Nokia and Qualcomm formally introduced the concept of authorised shared access (ASA), also known as licensed shared access (LSA), which is described by the EU Radio Spectrum Policy Group (RSPG) as, “An individual licensed regime of a limited number of licensees in a frequency band, already allocated to one or more incumbent users, for which the additional users are allowed to use the spectrum (or part of the spectrum) in accordance with sharing rules included in the rights of use of spectrum granted to the licensees, thereby allowing all the licensees to provide a certain level of QoS.” By establishing formal contractual agreements between license holders and “licensees” (amounting to some type of spectrum leasing), the first step of bringing incumbent operators and new entrants closer together was achieved, with the latter no longer considered as unreliable or “rogue.” On the technical front, the LSA system architecture relies on both a spectrum registry (LSA repository) where incumbents declare their spectrum occupancy, and a control unit (LSA controller) that handles the spectrum management and compliance. On the legal front, a legal framework was postulated in order to handle any kind of misbehavior of the licensees. Furthermore, it was the first time that QoS guarantees were given to the licensee. The introduction of ASA/LSA can therefore be viewed as an important breakthrough to make spectrum sharing a commercial reality.
As could be expected of course, the initial adoption of LSA was rather limited. For example, the initial version of LSA adopted by the European Conference of Postal and Telecommunication Administration (EC/CEPT) excluded concepts such as opportunistic spectrum access (OSA), typically secondary use or secondary service where the applicant has no protection from the PU. Moreover, according to this version, LSA applies only when the incumbent user(s) and the LSA licensees are of different natures (e.g., governmental versus commercial), operate different types of applications, and are subject to different regulatory constraints. Furthermore, the original version of LSA was geared mostly towards traditional mobile network operators (MNOs) as typical licensees, neglecting the various emerging vertical applications and new types of networks prescribed in fifth-generation (5G) technology. This was later improved by the introduction of evolved LSA (eLSA), which prescribes local area networks for use in cases such as industrial automation, e-health, and emergency services, among others (see Chapter 2).
The next important step came with the opening of the Citizens Broadband Radio Service (CBRS) in the frequency band 3.55–3.7 GHz by the Federal Communications Commission (FCC) in the USA, intended for spectrum sharing via a combination of licensed and unlicensed spectrum use. The corresponding system, pushed by both the Wireless Innovation Forum (WInnForum) and 3rd Generation Partnership Project (3GPP), is called the spectrum access system (SAS) and prescribes three tiers of users (operators): incumbents (such as radar systems), who enjoy exclusive spectrum usage, priority access license (PAL) users, who have exclusive access in the absence of the incumbent, and general authorized access (GAA) users, who have sensing-assisted unlicensed access in the absence of the incumbent (similar to traditional CR users). The availability of the released spectrum, backing from FCC, and inclusion of all three tiers of users make the use of SAS in the CBRS spectrum a strong contender for spectrum sharing-based access, in spite of the various remaining challenges and specifications that need to be met.
A brief comparison of the two dominant emerging types of spectrum sharing described above can be found below:
LSA (EU version)
Pushed by CEPT, ETSI, 3GPP
Two-tier model: incumbents, licensees
Spectrum sensing is country-wide
Incumbent protection through database
SAS (USA)
Pushed by FCC, 3GPP, WInnForum
Three-tier model: incumbents, PAL, GAA
Spectrum sensing in reduced areas (e.g., census tracks of 4000 people)
Interference mitigation across census tracts
Sensing-based protection of incumbents
More recently, another important trend arose: the coexistence of long-term evolution (LTE) and Wi-Fi. Trying to solve this and other important challenges, there has been a recent explosion of spectrum-sharing concepts: LTE in unlicensed bands (LTE-U), license-assisted access (LAA) in LTE advanced (LTE-A), LTE wireless local area network (WLAN) aggregation (LWA), LTE-WLAN radio level integration with Internet protocol security tunnel (LWIP), MulteFire, Wi-Fi in licensed band (Wi-Fi-Lic), Wi-Fi Boost, etc. (see Chapters 4 and 14). Given the availability of the corresponding LTE and Wi-Fi technologies, this approach is also well poised to affect spectrum access in the immediate future.
The culmination of these trends over the last decade constitutes a significant technology evolution (or possibly revolution) which we believe will affect the way the spectrum is accessed and used for a variety of applications and players in the forthcoming years, affecting both the economy and society. This edited volume is our attempt to collect the key concepts and emerging approaches, as well as to hint at the future impact of the important emerging field of spectrum sharing.
