IoT Signal Detection

Inbunden, Engelska, 2024

AvRui Han,Jingjing Wang,Lin Bai,Jianwei Liu,Beihang University) Han, Rui (School of Cyber Science and Technology,Beihang University) Wang, Jingjing (School of Cyber Science and Technology,Beihang University) Bai, Lin (School of Cyber Science and Technology,Beihang University) Liu, Jianwei (School of Cyber Science and Technology

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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 detectionSparse signal detection for multiple access, covering Bayesian compressive sensing algorithm and structured subspace pursuit algorithmCollided signal detection for multiple access using automatic modulation classification algorithm, round-trip delay estimation for collided signalsSignal detection for backscatter signals, covering central limited theorem-based detection including detection algorithms, performance analysis, and simulation resultsSignal design for multi-cluster coordination, covering successive interference cancellation design, device grouping and power control, and constructive interference-aided multi-cluster coordinationWith 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.

Produktinformation

Utgivningsdatum2024-10-25
Mått237 x 158 x 19 mm
Vikt553 g
FormatInbunden
SpråkEngelska
Antal sidor208
FörlagJohn Wiley & Sons Inc
ISBN9781394183081

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Elektronik och kommunikationer inom Naturvetenskap och teknik

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.

List of Figures xiList of Algorithms xviiAbout the Authors xixForeword xxiPreface xxiiiAcknowledgements xxvAcronyms xxvii1 Introduction 11.1 IoT in 5G 11.1.1 What Is IoT 11.1.2 Applications of IoT 21.1.3 Future of IoT 31.2 IoT Networks 41.3 Characteristics of IoT Signals 61.4 Outline 82 Background of IoT Signal Detection 112.1 Random Access 112.1.1 Grant-based Random Access 112.1.2 Grant-free Random Access 142.2 Signal Detection Methods 162.2.1 System Model 172.2.2 ML Detection 182.2.3 ZF Detection 222.2.4 MMSE Detection 252.2.5 MCMC Detection 282.2.6 VI Detection 312.2.7 CS Detection 342.3 Conclusion and Remarks 383 Sparse Signal Detection for Multiple Access 393.1 System Model 393.2 Sparse Signal Detection 413.2.1 Tree Search-based Approach 413.2.2 VI Detection Algorithm 443.3 Performance Analysis 483.3.1 Complexity Analysis 483.3.2 VI Detection Performance Analysis 493.4 Simulation Results 553.5 Conclusion and Remarks 614 Collided Signal Detection for Multiple Access 634.1 System Model 634.2 Automatic Modulation Classification-based Detection 664.2.1 Preamble Sequence Detection 664.2.2 HOCs-based AMC Approach for Collision Recognition 684.2.3 Data Decoding with SIC 694.3 Performance Analysis 714.4 Simulation Results 784.5 Conclusion and Remarks 865 Multiple Delay Estimation for Collided Signals 895.1 System Model 895.2 Multiple Delay Estimation 925.2.1 ML Detection Algorithm 925.2.2 CAVI Detection Algorithm 955.2.3 MCMC Detection Algorithm 995.3 Signal Number Estimation and Channel Estimation 1005.4 Simulation Results 1025.4.1 CAVI Simulation Results 1025.4.2 MCMC Simulation Results 1095.5 Conclusion and Remarks 1156 Detection and Division for Backscatter Signals 1176.1 System Model 1176.2 Central Limit Theorem-based Signal Detection 1226.2.1 Activity Detection Algorithm 1236.2.2 Signal Detection Algorithm 1266.2.3 Performance Analysis 1276.3 Simulation Results 1286.4 Conclusion and Remarks 1347 Analysis and Optimization for NOMA Signals 1377.1 System Model 1377.2 Throughput and Power Consumption Analysis 1397.2.1 Throughput Analysis 1397.2.2 Power Consumption Analysis 1407.3 Energy Efficiency Performance Optimization 1417.4 Simulation Results 1457.5 Conclusion and Remarks 1488 Signal Design for Multicluster Coordination 1498.1 Multi-cluster Coordination in IoT 1498.2 Multi-cluster Coordination with NOMA 1528.2.1 Multi-cluster Coordination NOMA Design 1528.2.2 Multi-cluster Coordinated NOMA Resource Allocation 1538.3 CI-aided Multi-cluster Coordination with Interference Management 1568.3.1 CI Signal Design 1568.3.2 CI Design for Multi-cluster Coordination 1588.4 FutureWorks 1618.5 Conclusion and Remarks 1629 Conclusion of the Book 163References 165Index 175