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Informationen zum Autor 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. Klappentext 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. Inhaltsverzeichnis 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 Collisi...
