Comprehensive reference covering signal detection for random access in IoT systems from the beginner to expert level With a carefully balanced blend of theoretical elements and applications, IoT Signal Detection is an easy-to-follow presentation on signal detection for IoT in terms of device activity detection, sparse signal detection, collided signal detection, round-trip delay estimation, and backscatter signal division, building progressively from basic concepts and important background material up to an advanced understanding of the subject. Various signal detection and estimation…mehr
Comprehensive reference covering signal detection for random access in IoT systems from the beginner to expert level With a carefully balanced blend of theoretical elements and applications, IoT Signal Detection is an easy-to-follow presentation on signal detection for IoT in terms of device activity detection, sparse signal detection, collided signal detection, round-trip delay estimation, and backscatter signal division, building progressively from basic concepts and important background material up to an advanced understanding of the subject. Various signal detection and estimation techniques are explained, e.g., variational inference algorithm and compressive sensing reconstruction algorithm, and a number of recent research outcomes are included to provide a review of the state of the art in the field. Written by four highly qualified academics, IoT Signal Detection discusses sample topics such as: * ML, ZF, and MMSE detection, Markov chain Monte Carlo-based detection, variational inference-based detection, compressive sensing-based detection * Sparse signal detection for multiple access, covering Bayesian compressive sensing algorithm and structured subspace pursuit algorithm * Collided signal detection for multiple access using automatic modulation classification algorithm, round-trip delay estimation for collided signals * Signal detection for backscatter signals, covering central limited theorem-based detection including detection algorithms, performance analysis, and simulation results * Signal design for multi-cluster coordination, covering successive interference cancellation design, device grouping and power control, and constructive interference-aided multi-cluster coordination With seamless coverage of the subject presented in a linear and easy-to-understand way, IoT Signal Detection is an ideal reference for both graduate students and practicing engineers in wireless communications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Rui Han, PhD, is an Associate Professor at the School of Cyber Science and Technology, Beihang University. Jingjing Wang, PhD, is a Professor at the School of Cyber Science and Technology, Beihang University. Lin Bai, PhD, is a Professor at the School of Cyber Science and Technology, Beihang University. Jianwei Liu, PhD, is a Professor at the School of Cyber Science and Technology, Beihang University.
Inhaltsangabe
List of Figures xi List of Algorithms xvii About the Authors xix Foreword xxi Preface xxiii Acknowledgements xxv Acronyms xxvii 1 Introduction 1 1.1 IoT in 5G 1 1.1.1 What Is IoT 1 1.1.2 Applications of IoT 2 1.1.3 Future of IoT 3 1.2 IoT Networks 4 1.3 Characteristics of IoT Signals 6 1.4 Outline 8 2 Background of IoT Signal Detection 11 2.1 Random Access 11 2.1.1 Grant-based Random Access 11 2.1.2 Grant-free Random Access 14 2.2 Signal Detection Methods 16 2.2.1 System Model 17 2.2.2 ML Detection 18 2.2.3 ZF Detection 22 2.2.4 MMSE Detection 25 2.2.5 MCMC Detection 28 2.2.6 VI Detection 31 2.2.7 CS Detection 34 2.3 Conclusion and Remarks 38 3 Sparse Signal Detection for Multiple Access 39 3.1 System Model 39 3.2 Sparse Signal Detection 41 3.2.1 Tree Search-based Approach 41 3.2.2 VI Detection Algorithm 44 3.3 Performance Analysis 48 3.3.1 Complexity Analysis 48 3.3.2 VI Detection Performance Analysis 49 3.4 Simulation Results 55 3.5 Conclusion and Remarks 61 4 Collided Signal Detection for Multiple Access 63 4.1 System Model 63 4.2 Automatic Modulation Classification-based Detection 66 4.2.1 Preamble Sequence Detection 66 4.2.2 HOCs-based AMC Approach for Collision Recognition 68 4.2.3 Data Decoding with SIC 69 4.3 Performance Analysis 71 4.4 Simulation Results 78 4.5 Conclusion and Remarks 86 5 Multiple Delay Estimation for Collided Signals 89 5.1 System Model 89 5.2 Multiple Delay Estimation 92 5.2.1 ML Detection Algorithm 92 5.2.2 CAVI Detection Algorithm 95 5.2.3 MCMC Detection Algorithm 99 5.3 Signal Number Estimation and Channel Estimation 100 5.4 Simulation Results 102 5.4.1 CAVI Simulation Results 102 5.4.2 MCMC Simulation Results 109 5.5 Conclusion and Remarks 115 6 Detection and Division for Backscatter Signals 117 6.1 System Model 117 6.2 Central Limit Theorem-based Signal Detection 122 6.2.1 Activity Detection Algorithm 123 6.2.2 Signal Detection Algorithm 126 6.2.3 Performance Analysis 127 6.3 Simulation Results 128 6.4 Conclusion and Remarks 134 7 Analysis and Optimization for NOMA Signals 137 7.1 System Model 137 7.2 Throughput and Power Consumption Analysis 139 7.2.1 Throughput Analysis 139 7.2.2 Power Consumption Analysis 140 7.3 Energy Efficiency Performance Optimization 141 7.4 Simulation Results 145 7.5 Conclusion and Remarks 148 8 Signal Design for Multicluster Coordination 149 8.1 Multi-cluster Coordination in IoT 149 8.2 Multi-cluster Coordination with NOMA 152 8.2.1 Multi-cluster Coordination NOMA Design 152 8.2.2 Multi-cluster Coordinated NOMA Resource Allocation 153 8.3 CI-aided Multi-cluster Coordination with Interference Management 156 8.3.1 CI Signal Design 156 8.3.2 CI Design for Multi-cluster Coordination 158 8.4 FutureWorks 161 8.5 Conclusion and Remarks 162 9 Conclusion of the Book 163 References 165 Index 175
