Details

Ultra-Dense Networks for 5G and Beyond


Ultra-Dense Networks for 5G and Beyond

Modelling, Analysis, and Applications
1. Aufl.

von: Trung Q. Duong, Xiaoli Chu, Himal A. Suraweera

114,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 31.01.2019
ISBN/EAN: 9781119473718
Sprache: englisch
Anzahl Seiten: 312

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Beschreibungen

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

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