List of Contributors xi Preface xv Part I Fundamentals of Ultra-dense Networks 1 1 Fundamental Limits of Ultra-dense Networks 3Marios Kountouris and Van Minh Nguyen 1.1 Introduction 3 1.2 System Model 6 1.2.1 Network Topology 6 1.2.2 Wireless Propagation Model 6 1.2.3 User Association 8 1.2.4 Performance Metrics 8 1.3 The Quest for Exact Analytical Expressions 9 1.3.1 Coverage Probability 10 1.3.2 The Effect of LOS Fading 16 1.3.3 The Effect of BS Height 19 1.4 The Quest for Scaling Laws 25 1.4.1 User Performance 26 1.4.2 Network Performance 33 1.4.3 Network Ordering and Design Guidelines 35 1.5 Conclusions and Future Challenges 36 Bibliography 37 2 Performance Analysis of Dense Small Cell Networks with Line of Sight and Non-Line of Sight Transmissions under Rician Fading 41Amir Hossein Jafari,Ming Ding and David López-Pérez 2.1 Introduction 41 2.2 System Model 42 2.2.1 BS Distribution 42 2.2.2 User Distribution 42 2.2.3 Path Loss 43 2.2.4 User Association Strategy (UAS) 44 2.2.5 Antenna Radiation Pattern 44 2.2.6 Multi-path Fading 44 2.3 Coverage Probability Analysis Based on the Piecewise Path Loss Model 44 2.4 Study of a 3GPP Special Case 46 2.4.1 The Computation of T1L 47 2.4.2 The Computation of T1NL 48 2.4.3 The Computation of T2 L 51 2.4.4 The Computation of T2 NL 51 2.4.5 The Results of pcov(𝜆, 𝛾) and AASE(𝜆, 𝛾0) 52 2.5 Simulation and Discussion 52 2.5.1 Validation of the Analytical Results of pcov(𝜆, 𝛾) for the 3GPP Case 52 2.5.2 Discussion on the Analytical Results of AASE(𝜆, 𝛾0) for the 3GPP Case 54 2.6 Conclusion 55 Appendix A: Proof ofTheorem 1.1 55 Appendix B: Proof of Lemma 2.2 60 Appendix C: Proof of Lemma 2.3 61 Appendix D: Proof of Lemma 2.4 62 Bibliography 62 3 Mean Field Games for 5G Ultra-dense Networks: A Resource Management Perspective 65Mbazingwa E.Mkiramweni, Chungang Yang and Zhu Han 3.1 Introduction 65 3.2 Literature Review 67 3.2.1 5G Ultra-dense Networks 67 3.2.2 Resource Management Challenges in 5G 71 3.2.3 Game Theory for Resource Management in 5G 71 3.3 Basics of Mean field game 71 3.3.1 Background 72 3.3.2 Mean Field Games 73 3.4 MFGs for D2D Communications in 5G 76 3.4.1 Applications of MFGs in 5G Ultra-dense D2D Networks 76 3.4.2 An Example of MFGs for Interference Management in UDN 77 3.5 MFGs for Radio Access Network in 5G 78 3.5.1 Application of MFGs for Radio Access Network in 5G 79 3.5.2 Energy Harvesting 81 3.5.3 An Example of MFGs for Radio Access Network in 5G 81 3.6 MFGs in 5G Edge Computing 84 3.6.1 MFG Applications in Edge Cloud Communication 85 3.7 Conclusion 85 Bibliography 85 Part II Ultra-dense Networks with Emerging 5G Technologies 91 4 Inband Full-duplex Self-backhauling in Ultra-dense Networks 93Dani Korpi, Taneli Riihonen and Mikko Valkama 4.1 Introduction 93 4.2 Self-backhauling in Existing Literature 94 4.3 Self-backhauling Strategies 95 4.3.1 Half-duplex Base Station without Access Nodes 97 4.3.2 Half-duplex Base Station with Half-duplex Access Nodes 97 4.3.3 Full-Duplex Base Station with Half-Duplex Access Nodes 98 4.3.4 Half-duplex Base Station with Full-duplex Access Nodes 99 4.4 Transmit Power Optimization under QoS Requirements 99 4.5 Performance Analysis 101 4.5.1 Simulation Setup 101 4.5.2 Numerical Results 103 4.6 Summary 109 Bibliography 110 5 The Role of Massive MIMO and Small Cells in Ultra-dense Networks 113Qi Zhang, Howard H. Yang and Tony Q. S. Quek 5.1 Introduction 113 5.2 System Model 115 5.2.1 Network Topology 115 5.2.2 Propagation Environment 116 5.2.3 User Association Policy 117 5.3 Average Downlink Rate 117 5.3.1 Association Probabilities 117 5.3.2 Uplink Training 119 5.3.3 Downlink Data Transmission 120 5.3.4 Approximation of Average Downlink Rate 121 5.4 Numerical Results 123 5.4.1 Validation of Analytical Results 123 5.4.2 Comparison between Massive MIMO and Small Cells 124 5.4.3 Optimal Network Configuration 126 5.5 Conclusion 127 Appendix 128 A.1 Proof of Theorem 5.1 128 A.2 Proof of Corollary 5.1 129 A.3 Proof of Theorem 5.2 129 A.4 Proof of Theorem 5.3 130 A.5 Proof of Proposition 5.1 130 A.6 Proof of Proposition 5.2 130 Bibliography 131 6 Security for Cell-free Massive MIMO Networks 135Tiep M. Hoang, Hien Quoc Ngo, Trung Q. Duong and Hoang D. Tuan 6.1 Introduction 135 6.2 Cell-free Massive MIMO System Model 136 6.3 Cell-free System Model in the presence of an active eavesdropper 139 6.4 On Dealing with Eavesdropper 143 6.4.1 Case 1: Power Coefficients Are Different 143 6.4.2 Case 2: Power Coefficients Are the Same 145 6.5 Numerical Results 146 6.6 Conclusion 148 Appendix 149 Bibliography 150 7 Massive MIMO for High-performance Ultra-dense Networks in the Unlicensed Spectrum 151Adrian Garcia-Rodriguez, Giovanni Geraci, Lorenzo Galati-Giordano and David López-Pérez 7.1 Introduction 151 7.2 System Model 152 7.3 Fundamentals of Massive MIMO Unlicensed (mMIMO-U) 154 7.3.1 Channel Covariance Estimation 154 7.3.2 Enhanced Listen Before Talk (eLBT) 155 7.3.3 Neighboring-Node-Aware Scheduling 157 7.3.4 Acquisition of Channel State Information 159 7.3.5 Beamforming with Radiation Nulls 160 7.4 Performance Evaluation 160 7.4.1 Outdoor Deployments 160 7.4.1.1 Cellular/Wi-Fi Coexistence 161 7.4.1.2 Achievable Cellular Data Rates 162 7.4.2 Indoor Deployments 165 7.4.2.1 Channel Access Success Rate 166 7.4.2.2 Downlink User SINR 166 7.4.2.3 Downlink Sum Throughput 169 7.5 Challenges 170 7.5.1 Wi-Fi Channel Subspace Estimation 170 7.5.2 Uplink Transmission 170 7.5.3 Hidden Terminals 171 7.6 Conclusion 172 Bibliography 172 8 Energy Efficiency Optimization for Dense Networks 175Quang-Doanh Vu, Markku Juntti, Een-Kee Hong and Le-Nam Tran 8.1 Introduction 175 8.2 Energy Efficiency Optimization Tools 176 8.2.1 Fractional Programming 176 8.2.2 Concave Fractional Programs 177 8.2.2.1 Parameterized Approach 177 8.2.2.2 Parameter-free Approach 178 8.2.3 Max–Min Fractional Programs 179 8.2.4 Generalized Non-convex Fractional Programs 179 8.2.5 Alternating Direction Method of Multipliers for Distributed Implementation 180 8.3 Energy Efficiency Optimization for Dense Networks: Case Studies 181 8.3.1 Multiple Radio Access Technologies 181 8.3.1.1 System Model and Energy Efficiency Maximization Problem 182 8.3.1.2 Solution via Parameterized Approach 184 8.3.1.3 Solution via Parameter-free Approach 184 8.3.1.4 Distributed Implementation 185 8.3.1.5 Numerical Examples 189 8.3.2 Dense Small Cell Networks 191 8.3.2.1 System Model 191 8.3.2.2 Centralized Solution via Successive Convex Approximation 193 8.3.2.3 Distributed Implementation 195 8.3.2.4 Numerical Examples 198 8.4 Conclusion 200 Bibliography 200 Part III Applications of Ultra-dense Networks 203 9 Big Data Methods for Ultra-dense Network Deployment 205Weisi Guo,Maria Liakata, GuillemMosquera,Weijie Qi, Jie Deng and Jie Zhang 9.1 Introduction 205 9.1.1 The Economic Case for Big Data in UDNs 205 9.1.2 Chapter Organization 207 9.2 Structured Data Analytics for Traffic Hotspot Characterization 207 9.2.1 Social Media Mapping of Hotspots 207 9.2.2 Community and Cluster Detection 211 9.2.3 Machine Learning for Clustering in Heterogeneous UDNs 213 9.3 Unstructured Data Analytics for Quality-of-Experience Mapping 219 9.3.1 Topic Identification 220 9.3.2 Sentiment 221 9.3.3 Data-Aware Wireless Network (DAWN) 222 9.4 Conclusion 226 Bibliography 227 10 Physical Layer Security for Ultra-dense Networks under Unreliable Backhaul Connection 231Huy T. Nguyen, Nam-Phong Nguyen, Trung Q. Duong andWon-Joo Hwang 10.1 Backhaul Reliability Level and Performance Limitation 232 10.1.1 Outage Probability Analysis under Backhaul Reliability Impacts 233 10.1.2 Performance Limitation 234 10.1.3 Numerical Results 234 10.2 Unreliable Backhaul Impacts with Physical Layer Security 235 10.2.1 The Two-Phase Transmitter/Relay Selection Scheme 237 10.2.2 Secrecy Outage Probability with Backhaul Reliability Impact 240 10.2.3 Secrecy Performance Limitation under Backhaul Reliability Impact 240 10.2.4 Numerical Results 241 Appendix A 242 Appendix B 243 Appendix C 244 Bibliography 245 11 SimultaneousWireless Information and Power Transfer in UDNs with Caching Architecture 247Sumit Gautam, Thang X. Vu, Symeon Chatzinotas and Björn Ottersten 11.1 Introduction 247 11.2 System Model 249 11.2.1 Signal Model 250 11.2.2 Caching Model 251 11.2.3 Power Assumption at the Relay 252 11.3 Maximization of the serving information rate 252 11.3.1 Optimization of TS Factors and the Relay Transmit Power 253 11.3.2 Relay Selection 255 11.4 Maximization of the Energy Stored at the Relay 255 11.4.1 Optimization of TS Factors and the Relay Transmit Power 256 11.4.2 Relay Selection 259 11.5 Numerical Results 260 11.6 Conclusion 263 Acknowledgment 265 Bibliography 265 12 Cooperative Video Streaming in Ultra-dense Networks with D2D Caching 267Nguyen-Son Vo and Trung Q. Duong 12.1 Introduction 267 12.2 5G Network with Dense D2D Caching for Video Streaming 268 12.2.1 System Model and Assumptions 269 12.2.2 Cooperative Transmission Strategy 270 12.2.3 Source Video Packetization Model 271 12.3 Problem Formulation and Solution 273 12.3.1 System Parameters Formulation 273 12.3.1.1 Average Reconstructed Distortion 273 12.3.1.2 Energy Consumption Guarantee 274 12.3.1.3 Co-channel Interference Guarantee 275 12.3.2 RDO Problem 275 12.3.3 GAs Solution 276 12.4 Performance Evaluation 276 12.4.1 D2D Caching 276 12.4.2 RDO 277 12.4.2.1 Simulation Setup 277 12.4.2.2 Performance Metrics 280 12.4.2.3 Discussions 285 12.5 Conclusion 285 Bibliography 285 Index 289

OFFERS COMPREHENSIVE INSIGHT INTO THE THEORY, MODELS, AND TECHNIQUES OF ULTRA-DENSE NETWORKS AND APPLICATIONS IN 5G AND OTHER EMERGING WIRELESS NETWORKS The need for speed—and power—in wireless communications is growing exponentially. Data rates are projected to increase by a factor of ten every five years—and with the emerging Internet of Things (IoT) predicted to wirelessly connect trillions of devices across the globe, future mobile networks (5G) will grind to a halt unless more capacity is created. This book presents new research related to the theory and practice of all aspects of ultra-dense networks, covering recent advances in ultra-dense networks for 5G networks and beyond, including cognitive radio networks, massive multiple-input multiple-output (MIMO), device-to-device (D2D) communications, millimeter-wave communications, and energy harvesting communications. Clear and concise throughout, Ultra-dense Networks for 5G and Beyond: Modelling, Analysis, and Applications offers comprehensive coverage on such topics as network optimization; mobility, handoff control, and interference management; and load balancing schemes and energy saving techniques. It delves into the backhaul traffic aspects in ultra-dense networks and studies transceiver hardware impairments and power consumption models in ultra-dense networks. The book also examines new IoT, smart-grid, and smart-city applications, as well as novel modulation, coding, and waveform designs. One of the first books to focus solely on ultra-dense networks for 5GCovers advanced architectures, self-organizing protocols, resource allocation, user-base station association, synchronization, and signallingExamines the current state of cell-free massive MIMO, distributed massive MIMO, and heterogeneous small cell architecturesOffers network measurements, implementations, and demosLooks at wireless caching techniques, physical layer security, cognitive radio, energy harvesting, and D2D communications in ultra-dense networks Ultra-dense Networks for 5G and Beyond: Modelling, Analysis, and Applications is an ideal reference for those who want to design high-speed, high-capacity communications in advanced networks, and will appeal to postgraduate students, researchers, and engineers in the field.