Ultra-Dense Networks for 5g and Beyond: Modelling, Analysis, and Applications

ISBN-13: 9781119473695 / Angielski / Twarda / 2019 / 312 str.

Trung Q. Duong; Xiaoli Chu; Himal A. Suraweera

Spis treści

List of Contributors xiPreface xvPart I Fundamentals of Ultra-dense Networks 11 Fundamental Limits of Ultra-dense Networks 3Marios Kountouris and Van Minh Nguyen1.1 Introduction 31.2 System Model 61.2.1 Network Topology 61.2.2 Wireless Propagation Model 61.2.3 User Association 81.2.4 Performance Metrics 81.3 The Quest for Exact Analytical Expressions 91.3.1 Coverage Probability 101.3.2 The Effect of LOS Fading 161.3.3 The Effect of BS Height 191.4 The Quest for Scaling Laws 251.4.1 User Performance 261.4.2 Network Performance 331.4.3 Network Ordering and Design Guidelines 351.5 Conclusions and Future Challenges 36Bibliography 372 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-Pérez2.1 Introduction 412.2 System Model 422.2.1 BS Distribution 422.2.2 User Distribution 422.2.3 Path Loss 432.2.4 User Association Strategy (UAS) 442.2.5 Antenna Radiation Pattern 442.2.6 Multi-path Fading 442.3 Coverage Probability Analysis Based on the Piecewise Path Loss Model 442.4 Study of a 3GPP Special Case 462.4.1 The Computation of T1¯L 472.4.2 The Computation of T1¯NL 482.4.3 The Computation of T2¯ L 512.4.4 The Computation of T2 ¯NL 512.4.5 The Results of p¯cov(lambda, gamma) and A¯ASE(lambda, gamma0) 522.5 Simulation and Discussion 522.5.1 Validation of the Analytical Results of p¯cov(lambda, gamma) for the 3GPP Case 522.5.2 Discussion on the Analytical Results of A¯ASE(lambda, gamma0) for the 3GPP Case 542.6 Conclusion 55Appendix A: Proof ofTheorem 1.1 55Appendix B: Proof of Lemma 2.2 60Appendix C: Proof of Lemma 2.3 61Appendix D: Proof of Lemma 2.4 62Bibliography 623 Mean Field Games for 5G Ultra-dense Networks: A Resource Management Perspective 65Mbazingwa E.Mkiramweni, Chungang Yang and Zhu Han3.1 Introduction 653.2 Literature Review 673.2.1 5G Ultra-dense Networks 673.2.2 Resource Management Challenges in 5G 713.2.3 Game Theory for Resource Management in 5G 713.3 Basics of Mean field game 713.3.1 Background 723.3.2 Mean Field Games 733.4 MFGs for D2D Communications in 5G 763.4.1 Applications of MFGs in 5G Ultra-dense D2D Networks 763.4.2 An Example of MFGs for Interference Management in UDN 773.5 MFGs for Radio Access Network in 5G 783.5.1 Application of MFGs for Radio Access Network in 5G 793.5.2 Energy Harvesting 813.5.3 An Example of MFGs for Radio Access Network in 5G 813.6 MFGs in 5G Edge Computing 843.6.1 MFG Applications in Edge Cloud Communication 853.7 Conclusion 85Bibliography 85Part II Ultra-dense Networks with Emerging 5G Technologies 914 Inband Full-duplex Self-backhauling in Ultra-dense Networks 93Dani Korpi, Taneli Riihonen and Mikko Valkama4.1 Introduction 934.2 Self-backhauling in Existing Literature 944.3 Self-backhauling Strategies 954.3.1 Half-duplex Base Station without Access Nodes 974.3.2 Half-duplex Base Station with Half-duplex Access Nodes 974.3.3 Full-Duplex Base Station with Half-Duplex Access Nodes 984.3.4 Half-duplex Base Station with Full-duplex Access Nodes 994.4 Transmit Power Optimization under QoS Requirements 994.5 Performance Analysis 1014.5.1 Simulation Setup 1014.5.2 Numerical Results 1034.6 Summary 109Bibliography 1105 The Role of Massive MIMO and Small Cells in Ultra-dense Networks 113Qi Zhang, Howard H. Yang and Tony Q. S. Quek5.1 Introduction 1135.2 System Model 1155.2.1 Network Topology 1155.2.2 Propagation Environment 1165.2.3 User Association Policy 1175.3 Average Downlink Rate 1175.3.1 Association Probabilities 1175.3.2 Uplink Training 1195.3.3 Downlink Data Transmission 1205.3.4 Approximation of Average Downlink Rate 1215.4 Numerical Results 1235.4.1 Validation of Analytical Results 1235.4.2 Comparison between Massive MIMO and Small Cells 1245.4.3 Optimal Network Configuration 1265.5 Conclusion 127Appendix 128A.1 Proof of Theorem 5.1 128A.2 Proof of Corollary 5.1 129A.3 Proof of Theorem 5.2 129A.4 Proof of Theorem 5.3 130A.5 Proof of Proposition 5.1 130A.6 Proof of Proposition 5.2 130Bibliography 1316 Security for Cell-free Massive MIMO Networks 135Tiep M. Hoang, Hien Quoc Ngo, Trung Q. Duong and Hoang D. Tuan6.1 Introduction 1356.2 Cell-free Massive MIMO System Model 1366.3 Cell-free System Model in the presence of an active eavesdropper 1396.4 On Dealing with Eavesdropper 1436.4.1 Case 1: Power Coefficients Are Different 1436.4.2 Case 2: Power Coefficients Are the Same 1456.5 Numerical Results 1466.6 Conclusion 148Appendix 149Bibliography 1507 Massive MIMO for High-performance Ultra-dense Networks in the Unlicensed Spectrum 151Adrian Garcia-Rodriguez, Giovanni Geraci, Lorenzo Galati-Giordano and David López-Pérez7.1 Introduction 1517.2 System Model 1527.3 Fundamentals of Massive MIMO Unlicensed (mMIMO-U) 1547.3.1 Channel Covariance Estimation 1547.3.2 Enhanced Listen Before Talk (eLBT) 1557.3.3 Neighboring-Node-Aware Scheduling 1577.3.4 Acquisition of Channel State Information 1597.3.5 Beamforming with Radiation Nulls 1607.4 Performance Evaluation 1607.4.1 Outdoor Deployments 1607.4.1.1 Cellular/Wi-Fi Coexistence 1617.4.1.2 Achievable Cellular Data Rates 1627.4.2 Indoor Deployments 1657.4.2.1 Channel Access Success Rate 1667.4.2.2 Downlink User SINR 1667.4.2.3 Downlink Sum Throughput 1697.5 Challenges 1707.5.1 Wi-Fi Channel Subspace Estimation 1707.5.2 Uplink Transmission 1707.5.3 Hidden Terminals 1717.6 Conclusion 172Bibliography 1728 Energy Efficiency Optimization for Dense Networks 175Quang-Doanh Vu, Markku Juntti, Een-Kee Hong and Le-Nam Tran8.1 Introduction 1758.2 Energy Efficiency Optimization Tools 1768.2.1 Fractional Programming 1768.2.2 Concave Fractional Programs 1778.2.2.1 Parameterized Approach 1778.2.2.2 Parameter-free Approach 1788.2.3 Max-Min Fractional Programs 1798.2.4 Generalized Non-convex Fractional Programs 1798.2.5 Alternating Direction Method of Multipliers for Distributed Implementation 1808.3 Energy Efficiency Optimization for Dense Networks: Case Studies 1818.3.1 Multiple Radio Access Technologies 1818.3.1.1 System Model and Energy Efficiency Maximization Problem 1828.3.1.2 Solution via Parameterized Approach 1848.3.1.3 Solution via Parameter-free Approach 1848.3.1.4 Distributed Implementation 1858.3.1.5 Numerical Examples 1898.3.2 Dense Small Cell Networks 1918.3.2.1 System Model 1918.3.2.2 Centralized Solution via Successive Convex Approximation 1938.3.2.3 Distributed Implementation 1958.3.2.4 Numerical Examples 1988.4 Conclusion 200Bibliography 200Part III Applications of Ultra-dense Networks 2039 Big Data Methods for Ultra-dense Network Deployment 205Weisi Guo,Maria Liakata, GuillemMosquera,Weijie Qi, Jie Deng and Jie Zhang9.1 Introduction 2059.1.1 The Economic Case for Big Data in UDNs 2059.1.2 Chapter Organization 2079.2 Structured Data Analytics for Traffic Hotspot Characterization 2079.2.1 Social Media Mapping of Hotspots 2079.2.2 Community and Cluster Detection 2119.2.3 Machine Learning for Clustering in Heterogeneous UDNs 2139.3 Unstructured Data Analytics for Quality-of-Experience Mapping 2199.3.1 Topic Identification 2209.3.2 Sentiment 2219.3.3 Data-Aware Wireless Network (DAWN) 2229.4 Conclusion 226Bibliography 22710 Physical Layer Security for Ultra-dense Networks under Unreliable Backhaul Connection 231Huy T. Nguyen, Nam-Phong Nguyen, Trung Q. Duong andWon-Joo Hwang10.1 Backhaul Reliability Level and Performance Limitation 23210.1.1 Outage Probability Analysis under Backhaul Reliability Impacts 23310.1.2 Performance Limitation 23410.1.3 Numerical Results 23410.2 Unreliable Backhaul Impacts with Physical Layer Security 23510.2.1 The Two-Phase Transmitter/Relay Selection Scheme 23710.2.2 Secrecy Outage Probability with Backhaul Reliability Impact 24010.2.3 Secrecy Performance Limitation under Backhaul Reliability Impact 24010.2.4 Numerical Results 241Appendix A 242Appendix B 243Appendix C 244Bibliography 24511 SimultaneousWireless Information and Power Transfer in UDNs with Caching Architecture 247Sumit Gautam, Thang X. Vu, Symeon Chatzinotas and Björn Ottersten11.1 Introduction 24711.2 System Model 24911.2.1 Signal Model 25011.2.2 Caching Model 25111.2.3 Power Assumption at the Relay 25211.3 Maximization of the serving information rate 25211.3.1 Optimization of TS Factors and the Relay Transmit Power 25311.3.2 Relay Selection 25511.4 Maximization of the Energy Stored at the Relay 25511.4.1 Optimization of TS Factors and the Relay Transmit Power 25611.4.2 Relay Selection 25911.5 Numerical Results 26011.6 Conclusion 263Acknowledgment 265Bibliography 26512 Cooperative Video Streaming in Ultra-dense Networks with D2D Caching 267Nguyen-Son Vo and Trung Q. Duong12.1 Introduction 26712.2 5G Network with Dense D2D Caching for Video Streaming 26812.2.1 System Model and Assumptions 26912.2.2 Cooperative Transmission Strategy 27012.2.3 Source Video Packetization Model 27112.3 Problem Formulation and Solution 27312.3.1 System Parameters Formulation 27312.3.1.1 Average Reconstructed Distortion 27312.3.1.2 Energy Consumption Guarantee 27412.3.1.3 Co-channel Interference Guarantee 27512.3.2 RDO Problem 27512.3.3 GAs Solution 27612.4 Performance Evaluation 27612.4.1 D2D Caching 27612.4.2 RDO 27712.4.2.1 Simulation Setup 27712.4.2.2 Performance Metrics 28012.4.2.3 Discussions 28512.5 Conclusion 285Bibliography 285Index 289

O autorze

TRUNG Q. DUONG, PHD, is a Reader at Queen's University Belfast, UK, and is currently serving as an Editor for IEEE Transactions on Wireless Communications and IEEE Transactions on Communications.XIAOLI CHU, PHD, is a Reader at the University of Sheffield, UK, and is an Editor for the IEEE Wireless Communications Letters and the IEEE Communications Letters.HIMAL A. SURAWEERA, PHD, is a Senior Lecturer at the University of Peradeniya, Sri Lanka, and serves as an Editor of the IEEE Transactions on Wireless Communications, IEEE Transactions on Communications and IEEE Transactions on Green Communications and Networking.