{"product_id":"ultra-dense-networks-for-5g-and-beyond-isbn-9781119473695","title":"Ultra-Dense Networks for 5G and Beyond","description":"\u003cp\u003e\u003cb\u003eOffers comprehensive insight into the theory, models, and techniques of ultra-dense networks and applications in 5G and other emerging wireless networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eClear and concise throughout, \u003ci\u003eUltra-Dense Networks for 5G and Beyond - Modelling, Analysis, and Applications \u003c\/i\u003eoffers a 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOne of the first books to focus solely on ultra-dense networks for 5G in a complete presentation\u003c\/li\u003e \u003cli\u003eCovers advanced architectures, self-organizing protocols, resource allocation, user-base station association, synchronization, and signaling\u003c\/li\u003e \u003cli\u003eExamines the current state of cell-free massive MIMO, distributed massive MIMO, and heterogeneous small cell architectures\u003c\/li\u003e \u003cli\u003eOffers network measurements, implementations, and demos\u003c\/li\u003e \u003cli\u003eLooks at wireless caching techniques, physical layer security, cognitive radio, energy harvesting, and D2D communications in ultra-dense networks\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eUltra-Dense Networks for 5G and Beyond - Modelling, Analysis, and Applications\u003c\/i\u003e 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. \u003c\/p\u003e \u003cp\u003eList of Contributors xi\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Fundamentals of Ultra-dense Networks 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Fundamental Limits of Ultra-dense Networks 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMarios Kountouris and Van Minh Nguyen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 System Model 6\u003c\/p\u003e \u003cp\u003e1.2.1 Network Topology 6\u003c\/p\u003e \u003cp\u003e1.2.2 Wireless Propagation Model 6\u003c\/p\u003e \u003cp\u003e1.2.3 User Association 8\u003c\/p\u003e \u003cp\u003e1.2.4 Performance Metrics 8\u003c\/p\u003e \u003cp\u003e1.3 The Quest for Exact Analytical Expressions 9\u003c\/p\u003e \u003cp\u003e1.3.1 Coverage Probability 10\u003c\/p\u003e \u003cp\u003e1.3.2 The Effect of LOS Fading 16\u003c\/p\u003e \u003cp\u003e1.3.3 The Effect of BS Height 19\u003c\/p\u003e \u003cp\u003e1.4 The Quest for Scaling Laws 25\u003c\/p\u003e \u003cp\u003e1.4.1 User Performance 26\u003c\/p\u003e \u003cp\u003e1.4.2 Network Performance 33\u003c\/p\u003e \u003cp\u003e1.4.3 Network Ordering and Design Guidelines 35\u003c\/p\u003e \u003cp\u003e1.5 Conclusions and Future Challenges 36\u003c\/p\u003e \u003cp\u003eBibliography 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Performance Analysis of Dense Small Cell Networks with Line of Sight and Non-Line of Sight\u003c\/b\u003e \u003cb\u003eTransmissions under Rician Fading 41\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAmir Hossein Jafari,Ming Ding and David López-Pérez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 41\u003c\/p\u003e \u003cp\u003e2.2 System Model 42\u003c\/p\u003e \u003cp\u003e2.2.1 BS Distribution 42\u003c\/p\u003e \u003cp\u003e2.2.2 User Distribution 42\u003c\/p\u003e \u003cp\u003e2.2.3 Path Loss 43\u003c\/p\u003e \u003cp\u003e2.2.4 User Association Strategy (UAS) 44\u003c\/p\u003e \u003cp\u003e2.2.5 Antenna Radiation Pattern 44\u003c\/p\u003e \u003cp\u003e2.2.6 Multi-path Fading 44\u003c\/p\u003e \u003cp\u003e2.3 Coverage Probability Analysis Based on the Piecewise Path Loss Model 44\u003c\/p\u003e \u003cp\u003e2.4 Study of a 3GPP Special Case 46\u003c\/p\u003e \u003cp\u003e2.4.1 The Computation of T\u003csub\u003e1\u003c\/sub\u003e\u003csup\u003eL\u003c\/sup\u003e 47\u003c\/p\u003e \u003cp\u003e2.4.2 The Computation of T\u003csub\u003e1\u003c\/sub\u003e\u003csup\u003eNL\u003c\/sup\u003e 48\u003c\/p\u003e \u003cp\u003e2.4.3 The Computation of T\u003csub\u003e2\u003c\/sub\u003e\u003csup\u003e L\u003c\/sup\u003e 51\u003c\/p\u003e \u003cp\u003e2.4.4 The Computation of T\u003csub\u003e2 \u003c\/sub\u003e\u003csup\u003eNL\u003c\/sup\u003e 51\u003c\/p\u003e \u003cp\u003e2.4.5 The Results of p\u003csup\u003ecov\u003c\/sup\u003e(𝜆, 𝛾) and A\u003csup\u003eASE\u003c\/sup\u003e(𝜆, 𝛾0) 52\u003c\/p\u003e \u003cp\u003e2.5 Simulation and Discussion 52\u003c\/p\u003e \u003cp\u003e2.5.1 Validation of the Analytical Results of p\u003csup\u003ecov\u003c\/sup\u003e(𝜆, 𝛾) for the 3GPP Case 52\u003c\/p\u003e \u003cp\u003e2.5.2 Discussion on the Analytical Results of A\u003csup\u003eASE\u003c\/sup\u003e(𝜆, 𝛾0) for the 3GPP Case 54\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 55\u003c\/p\u003e \u003cp\u003eAppendix A: Proof ofTheorem 1.1 55\u003c\/p\u003e \u003cp\u003eAppendix B: Proof of Lemma 2.2 60\u003c\/p\u003e \u003cp\u003eAppendix C: Proof of Lemma 2.3 61\u003c\/p\u003e \u003cp\u003eAppendix D: Proof of Lemma 2.4 62\u003c\/p\u003e \u003cp\u003eBibliography 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Mean Field Games for 5G Ultra-dense Networks: A Resource Management Perspective 65\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMbazingwa E.Mkiramweni, Chungang Yang and Zhu Han\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 65\u003c\/p\u003e \u003cp\u003e3.2 Literature Review 67\u003c\/p\u003e \u003cp\u003e3.2.1 5G Ultra-dense Networks 67\u003c\/p\u003e \u003cp\u003e3.2.2 Resource Management Challenges in 5G 71\u003c\/p\u003e \u003cp\u003e3.2.3 Game Theory for Resource Management in 5G 71\u003c\/p\u003e \u003cp\u003e3.3 Basics of Mean field game 71\u003c\/p\u003e \u003cp\u003e3.3.1 Background 72\u003c\/p\u003e \u003cp\u003e3.3.2 Mean Field Games 73\u003c\/p\u003e \u003cp\u003e3.4 MFGs for D2D Communications in 5G 76\u003c\/p\u003e \u003cp\u003e3.4.1 Applications of MFGs in 5G Ultra-dense D2D Networks 76\u003c\/p\u003e \u003cp\u003e3.4.2 An Example of MFGs for Interference Management in UDN 77\u003c\/p\u003e \u003cp\u003e3.5 MFGs for Radio Access Network in 5G 78\u003c\/p\u003e \u003cp\u003e3.5.1 Application of MFGs for Radio Access Network in 5G 79\u003c\/p\u003e \u003cp\u003e3.5.2 Energy Harvesting 81\u003c\/p\u003e \u003cp\u003e3.5.3 An Example of MFGs for Radio Access Network in 5G 81\u003c\/p\u003e \u003cp\u003e3.6 MFGs in 5G Edge Computing 84\u003c\/p\u003e \u003cp\u003e3.6.1 MFG Applications in Edge Cloud Communication 85\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 85\u003c\/p\u003e \u003cp\u003eBibliography 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Ultra-dense Networks with Emerging 5G Technologies 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Inband Full-duplex Self-backhauling in Ultra-dense Networks 93\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDani Korpi, Taneli Riihonen and Mikko Valkama\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 93\u003c\/p\u003e \u003cp\u003e4.2 Self-backhauling in Existing Literature 94\u003c\/p\u003e \u003cp\u003e4.3 Self-backhauling Strategies 95\u003c\/p\u003e \u003cp\u003e4.3.1 Half-duplex Base Station without Access Nodes 97\u003c\/p\u003e \u003cp\u003e4.3.2 Half-duplex Base Station with Half-duplex Access Nodes 97\u003c\/p\u003e \u003cp\u003e4.3.3 Full-Duplex Base Station with Half-Duplex Access Nodes 98\u003c\/p\u003e \u003cp\u003e4.3.4 Half-duplex Base Station with Full-duplex Access Nodes 99\u003c\/p\u003e \u003cp\u003e4.4 Transmit Power Optimization under QoS Requirements 99\u003c\/p\u003e \u003cp\u003e4.5 Performance Analysis 101\u003c\/p\u003e \u003cp\u003e4.5.1 Simulation Setup 101\u003c\/p\u003e \u003cp\u003e4.5.2 Numerical Results 103\u003c\/p\u003e \u003cp\u003e4.6 Summary 109\u003c\/p\u003e \u003cp\u003eBibliography 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The Role of Massive MIMO and Small Cells in Ultra-dense Networks 113\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eQi Zhang, Howard H. Yang and Tony Q. S. Quek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 113\u003c\/p\u003e \u003cp\u003e5.2 System Model 115\u003c\/p\u003e \u003cp\u003e5.2.1 Network Topology 115\u003c\/p\u003e \u003cp\u003e5.2.2 Propagation Environment 116\u003c\/p\u003e \u003cp\u003e5.2.3 User Association Policy 117\u003c\/p\u003e \u003cp\u003e5.3 Average Downlink Rate 117\u003c\/p\u003e \u003cp\u003e5.3.1 Association Probabilities 117\u003c\/p\u003e \u003cp\u003e5.3.2 Uplink Training 119\u003c\/p\u003e \u003cp\u003e5.3.3 Downlink Data Transmission 120\u003c\/p\u003e \u003cp\u003e5.3.4 Approximation of Average Downlink Rate 121\u003c\/p\u003e \u003cp\u003e5.4 Numerical Results 123\u003c\/p\u003e \u003cp\u003e5.4.1 Validation of Analytical Results 123\u003c\/p\u003e \u003cp\u003e5.4.2 Comparison between Massive MIMO and Small Cells 124\u003c\/p\u003e \u003cp\u003e5.4.3 Optimal Network Configuration 126\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix 128\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Proof of Theorem 5.1 128\u003c\/p\u003e \u003cp\u003eA.2 Proof of Corollary 5.1 129\u003c\/p\u003e \u003cp\u003eA.3 Proof of Theorem 5.2 129\u003c\/p\u003e \u003cp\u003eA.4 Proof of Theorem 5.3 130\u003c\/p\u003e \u003cp\u003eA.5 Proof of Proposition 5.1 130\u003c\/p\u003e \u003cp\u003eA.6 Proof of Proposition 5.2 130\u003c\/p\u003e \u003cp\u003eBibliography 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Security for Cell-free Massive MIMO Networks 135\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTiep M. Hoang, Hien Quoc Ngo, Trung Q. Duong and Hoang D. Tuan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 135\u003c\/p\u003e \u003cp\u003e6.2 Cell-free Massive MIMO System Model 136\u003c\/p\u003e \u003cp\u003e6.3 Cell-free System Model in the presence of an active eavesdropper 139\u003c\/p\u003e \u003cp\u003e6.4 On Dealing with Eavesdropper 143\u003c\/p\u003e \u003cp\u003e6.4.1 Case 1: Power Coefficients Are Different 143\u003c\/p\u003e \u003cp\u003e6.4.2 Case 2: Power Coefficients Are the Same 145\u003c\/p\u003e \u003cp\u003e6.5 Numerical Results 146\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 148\u003c\/p\u003e \u003cp\u003eAppendix 149\u003c\/p\u003e \u003cp\u003eBibliography 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Massive MIMO for High-performance Ultra-dense Networks in the Unlicensed Spectrum 151\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAdrian Garcia-Rodriguez, Giovanni Geraci, Lorenzo Galati-Giordano and David López-Pérez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 151\u003c\/p\u003e \u003cp\u003e7.2 System Model 152\u003c\/p\u003e \u003cp\u003e7.3 Fundamentals of Massive MIMO Unlicensed (mMIMO-U) 154\u003c\/p\u003e \u003cp\u003e7.3.1 Channel Covariance Estimation 154\u003c\/p\u003e \u003cp\u003e7.3.2 Enhanced Listen Before Talk (eLBT) 155\u003c\/p\u003e \u003cp\u003e7.3.3 Neighboring-Node-Aware Scheduling 157\u003c\/p\u003e \u003cp\u003e7.3.4 Acquisition of Channel State Information 159\u003c\/p\u003e \u003cp\u003e7.3.5 Beamforming with Radiation Nulls 160\u003c\/p\u003e \u003cp\u003e7.4 Performance Evaluation 160\u003c\/p\u003e \u003cp\u003e7.4.1 Outdoor Deployments 160\u003c\/p\u003e \u003cp\u003e7.4.1.1 Cellular\/Wi-Fi Coexistence 161\u003c\/p\u003e \u003cp\u003e7.4.1.2 Achievable Cellular Data Rates 162\u003c\/p\u003e \u003cp\u003e7.4.2 Indoor Deployments 165\u003c\/p\u003e \u003cp\u003e7.4.2.1 Channel Access Success Rate 166\u003c\/p\u003e \u003cp\u003e7.4.2.2 Downlink User SINR 166\u003c\/p\u003e \u003cp\u003e7.4.2.3 Downlink Sum Throughput 169\u003c\/p\u003e \u003cp\u003e7.5 Challenges 170\u003c\/p\u003e \u003cp\u003e7.5.1 Wi-Fi Channel Subspace Estimation 170\u003c\/p\u003e \u003cp\u003e7.5.2 Uplink Transmission 170\u003c\/p\u003e \u003cp\u003e7.5.3 Hidden Terminals 171\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 172\u003c\/p\u003e \u003cp\u003eBibliography 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Energy Efficiency Optimization for Dense Networks 175\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eQuang-Doanh Vu, Markku Juntti, Een-Kee Hong and Le-Nam Tran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 175\u003c\/p\u003e \u003cp\u003e8.2 Energy Efficiency Optimization Tools 176\u003c\/p\u003e \u003cp\u003e8.2.1 Fractional Programming 176\u003c\/p\u003e \u003cp\u003e8.2.2 Concave Fractional Programs 177\u003c\/p\u003e \u003cp\u003e8.2.2.1 Parameterized Approach 177\u003c\/p\u003e \u003cp\u003e8.2.2.2 Parameter-free Approach 178\u003c\/p\u003e \u003cp\u003e8.2.3 Max–Min Fractional Programs 179\u003c\/p\u003e \u003cp\u003e8.2.4 Generalized Non-convex Fractional Programs 179\u003c\/p\u003e \u003cp\u003e8.2.5 Alternating Direction Method of Multipliers for Distributed Implementation 180\u003c\/p\u003e \u003cp\u003e8.3 Energy Efficiency Optimization for Dense Networks: Case Studies 181\u003c\/p\u003e \u003cp\u003e8.3.1 Multiple Radio Access Technologies 181\u003c\/p\u003e \u003cp\u003e8.3.1.1 System Model and Energy Efficiency Maximization Problem 182\u003c\/p\u003e \u003cp\u003e8.3.1.2 Solution via Parameterized Approach 184\u003c\/p\u003e \u003cp\u003e8.3.1.3 Solution via Parameter-free Approach 184\u003c\/p\u003e \u003cp\u003e8.3.1.4 Distributed Implementation 185\u003c\/p\u003e \u003cp\u003e8.3.1.5 Numerical Examples 189\u003c\/p\u003e \u003cp\u003e8.3.2 Dense Small Cell Networks 191\u003c\/p\u003e \u003cp\u003e8.3.2.1 System Model 191\u003c\/p\u003e \u003cp\u003e8.3.2.2 Centralized Solution via Successive Convex Approximation 193\u003c\/p\u003e \u003cp\u003e8.3.2.3 Distributed Implementation 195\u003c\/p\u003e \u003cp\u003e8.3.2.4 Numerical Examples 198\u003c\/p\u003e \u003cp\u003e8.4 Conclusion 200\u003c\/p\u003e \u003cp\u003eBibliography 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Applications of Ultra-dense Networks 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Big Data Methods for Ultra-dense Network Deployment 205\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWeisi Guo,Maria Liakata, GuillemMosquera,Weijie Qi, Jie Deng and Jie Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 205\u003c\/p\u003e \u003cp\u003e9.1.1 The Economic Case for Big Data in UDNs 205\u003c\/p\u003e \u003cp\u003e9.1.2 Chapter Organization 207\u003c\/p\u003e \u003cp\u003e9.2 Structured Data Analytics for Traffic Hotspot Characterization 207\u003c\/p\u003e \u003cp\u003e9.2.1 Social Media Mapping of Hotspots 207\u003c\/p\u003e \u003cp\u003e9.2.2 Community and Cluster Detection 211\u003c\/p\u003e \u003cp\u003e9.2.3 Machine Learning for Clustering in Heterogeneous UDNs 213\u003c\/p\u003e \u003cp\u003e9.3 Unstructured Data Analytics for Quality-of-Experience Mapping 219\u003c\/p\u003e \u003cp\u003e9.3.1 Topic Identification 220\u003c\/p\u003e \u003cp\u003e9.3.2 Sentiment 221\u003c\/p\u003e \u003cp\u003e9.3.3 Data-Aware Wireless Network (DAWN) 222\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 226\u003c\/p\u003e \u003cp\u003eBibliography 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Physical Layer Security for Ultra-dense Networks under Unreliable Backhaul Connection 231\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHuy T. Nguyen, Nam-Phong Nguyen, Trung Q. Duong andWon-Joo Hwang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Backhaul Reliability Level and Performance Limitation 232\u003c\/p\u003e \u003cp\u003e10.1.1 Outage Probability Analysis under Backhaul Reliability Impacts 233\u003c\/p\u003e \u003cp\u003e10.1.2 Performance Limitation 234\u003c\/p\u003e \u003cp\u003e10.1.3 Numerical Results 234\u003c\/p\u003e \u003cp\u003e10.2 Unreliable Backhaul Impacts with Physical Layer Security 235\u003c\/p\u003e \u003cp\u003e10.2.1 The Two-Phase Transmitter\/Relay Selection Scheme 237\u003c\/p\u003e \u003cp\u003e10.2.2 Secrecy Outage Probability with Backhaul Reliability Impact 240\u003c\/p\u003e \u003cp\u003e10.2.3 Secrecy Performance Limitation under Backhaul Reliability Impact 240\u003c\/p\u003e \u003cp\u003e10.2.4 Numerical Results 241\u003c\/p\u003e \u003cp\u003eAppendix A 242\u003c\/p\u003e \u003cp\u003eAppendix B 243\u003c\/p\u003e \u003cp\u003eAppendix C 244\u003c\/p\u003e \u003cp\u003eBibliography 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 SimultaneousWireless Information and Power Transfer in UDNs with Caching Architecture 247\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSumit Gautam, Thang X. Vu, Symeon Chatzinotas and Björn Ottersten\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 247\u003c\/p\u003e \u003cp\u003e11.2 System Model 249\u003c\/p\u003e \u003cp\u003e11.2.1 Signal Model 250\u003c\/p\u003e \u003cp\u003e11.2.2 Caching Model 251\u003c\/p\u003e \u003cp\u003e11.2.3 Power Assumption at the Relay 252\u003c\/p\u003e \u003cp\u003e11.3 Maximization of the serving information rate 252\u003c\/p\u003e \u003cp\u003e11.3.1 Optimization of TS Factors and the Relay Transmit Power 253\u003c\/p\u003e \u003cp\u003e11.3.2 Relay Selection 255\u003c\/p\u003e \u003cp\u003e11.4 Maximization of the Energy Stored at the Relay 255\u003c\/p\u003e \u003cp\u003e11.4.1 Optimization of TS Factors and the Relay Transmit Power 256\u003c\/p\u003e \u003cp\u003e11.4.2 Relay Selection 259\u003c\/p\u003e \u003cp\u003e11.5 Numerical Results 260\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 263\u003c\/p\u003e \u003cp\u003eAcknowledgment 265\u003c\/p\u003e \u003cp\u003eBibliography 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Cooperative Video Streaming in Ultra-dense Networks with D2D Caching 267\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNguyen-Son Vo and Trung Q. Duong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 267\u003c\/p\u003e \u003cp\u003e12.2 5G Network with Dense D2D Caching for Video Streaming 268\u003c\/p\u003e \u003cp\u003e12.2.1 System Model and Assumptions 269\u003c\/p\u003e \u003cp\u003e12.2.2 Cooperative Transmission Strategy 270\u003c\/p\u003e \u003cp\u003e12.2.3 Source Video Packetization Model 271\u003c\/p\u003e \u003cp\u003e12.3 Problem Formulation and Solution 273\u003c\/p\u003e \u003cp\u003e12.3.1 System Parameters Formulation 273\u003c\/p\u003e \u003cp\u003e12.3.1.1 Average Reconstructed Distortion 273\u003c\/p\u003e \u003cp\u003e12.3.1.2 Energy Consumption Guarantee 274\u003c\/p\u003e \u003cp\u003e12.3.1.3 Co-channel Interference Guarantee 275\u003c\/p\u003e \u003cp\u003e12.3.2 RDO Problem 275\u003c\/p\u003e \u003cp\u003e12.3.3 GAs Solution 276\u003c\/p\u003e \u003cp\u003e12.4 Performance Evaluation 276\u003c\/p\u003e \u003cp\u003e12.4.1 D2D Caching 276\u003c\/p\u003e \u003cp\u003e12.4.2 RDO 277\u003c\/p\u003e \u003cp\u003e12.4.2.1 Simulation Setup 277\u003c\/p\u003e \u003cp\u003e12.4.2.2 Performance Metrics 280\u003c\/p\u003e \u003cp\u003e12.4.2.3 Discussions 285\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 285\u003c\/p\u003e \u003cp\u003eBibliography 285\u003c\/p\u003e \u003cp\u003eIndex 289\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eTRUNG Q. DUONG, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a Reader at Queen's University Belfast, UK, and is currently serving as an Editor for \u003ci\u003eIEEE Transactions on Wireless Communications\u003c\/i\u003e and \u003ci\u003eIEEE Transactions on Communications.\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eXIAOLI CHU, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a Reader at the University of Sheffield, UK, and is an Editor for the \u003ci\u003eIEEE Wireless Communications Letters\u003c\/i\u003e and the \u003ci\u003eIEEE Communications Letters.\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eHIMAL A. SURAWEERA, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a Senior Lecturer at the University of Peradeniya, Sri Lanka, and serves as an Editor of the \u003ci\u003eIEEE Transactions on Wireless Communications, IEEE Transactions on Communications\u003c\/i\u003e and \u003ci\u003eIEEE Transactions on Green Communications and Networking.\u003c\/i\u003e\t   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eOFFERS COMPREHENSIVE INSIGHT INTO THE THEORY, MODELS, AND TECHNIQUES OF ULTRA-DENSE NETWORKS AND APPLICATIONS IN 5G AND OTHER EMERGING WIRELESS NETWORKS\u003c\/b\u003e\t \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eClear and concise throughout,\u003ci\u003e Ultra-dense Networks for 5G and Beyond: Modelling, Analysis, and Applications\u003c\/i\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eOne of the first books to focus solely on ultra-dense networks for 5G\u003c\/li\u003e \u003cli\u003eCovers advanced architectures, self-organizing protocols, resource allocation,   user-base station association, synchronization, and signalling\u003c\/li\u003e \u003cli\u003eExamines the current state of cell-free massive MIMO, distributed massive MIMO,   and heterogeneous small cell architectures\u003c\/li\u003e \u003cli\u003eOffers network measurements, implementations, and demos\u003c\/li\u003e \u003cli\u003eLooks at wireless caching techniques, physical layer security, cognitive radio, energy   harvesting, and D2D communications in ultra-dense networks\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eUltra-dense Networks for 5G and Beyond: Modelling, Analysis, and Applications\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990423093477,"sku":"NP9781119473695","price":145.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119473695.jpg?v=1761787766","url":"https:\/\/k12savings.com\/es\/products\/ultra-dense-networks-for-5g-and-beyond-isbn-9781119473695","provider":"K12savings","version":"1.0","type":"link"}