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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
List of Figures
List of Algorithms
About the Authors
Foreword
Preface
Acknowledgements
Acronyms
1 Introduction
1.1 IoT in 5G
1.2 IoT Networks
1.3 Characteristics of IoT Signals
1.4 Outline
2 Background of IoT Signal Detection
2.1 Random Access
2.2 Signal Detection Methods
2.3 Conclusion and Remarks
3 Sparse Signal Detection for Multiple Access
3.1 System Model
3.2 Sparse Signal Detection
3.3 Performance Analysis
3.4 Simulation Results
3.5 Conclusion and Remarks
4 Collided Signal Detection for Multiple Access
4.1 System Model
4.2 Automatic Modulation Classification-based Detection
4.3 Performance Analysis
4.4 Simulation Results
4.5 Conclusion and Remarks
5 Multiple Delay Estimation for Collided Signals
5.1 System Model
5.2 Multiple Delay Estimation
5.3 Signal Number Estimation and Channel Estimation
5.4 Simulation Results
5.5 Conclusion and Remarks
Notes
6 Detection and Division for Backscatter Signals
6.1 System Model
6.2 Central Limit Theorem-based Signal Detection
6.3 Simulation Results
6.4 Conclusion and Remarks
7 Analysis and Optimization for NOMA Signals
7.1 System Model
7.2 Throughput and Power Consumption Analysis
7.3 Energy Efficiency Performance Optimization
7.4 Simulation Results
7.5 Conclusion and Remarks
Note
8 Signal Design for Multicluster Coordination
8.1 Multi-cluster Coordination in IoT
8.2 Multi-cluster Coordination with NOMA
8.3 CI-aided Multi-cluster Coordination with Interference Management
8.4 Future Works
8.5 Conclusion and Remarks
9 Conclusion of the Book
References
Index
End User License Agreement
List of Tables
Chapter 4
Table 4.1 Parameters Setting.
Chapter 7
Table 7.1 Simulation Setup.
List of Illustrations
Chapter 1
Figure 1.1 Architecture of IoT.
Chapter 2
Figure 2.1 The contention-based RA.
Figure 2.2 The contention-free RA.
Figure 2.3 The grant-free RA.
Figure 2.4 The compressed sensing.
Figure 2.5 A MIMO system.
Figure 2.6 The constellation of - system with ML.
Figure 2.7 BER of ML detector in - MIMO system.
Figure 2.8 The constellation of - system with ZF.
Figure 2.9 BER of ZF detector in - MIMO system.
Figure 2.10 The process of MMSE detection.
Figure 2.11 The constellation of - system with MMSE.
Figure 2.12 BER of MMSE detector in - system.
Figure 2.13 Comparison between ZF detector and MCMC detector when .
Figure 2.14 BER of MCMC detector in - MIMO system.
Figure 2.15 BER of VI detection in MIMO system.
Figure 2.16 The process of compressive sensing.
Figure 2.17 The reconstruction residual with different observation vector si...
Chapter 3
Figure 3.1 Structure of NGMA systems.
Figure 3.2 The tree search for the presence of (sparse) signals in a block....
Figure 3.3 Complexity ratio: (a) as a function of with ; (b) as a functio...
Figure 3.4 -divergence and its approximation in Eq. (3.59) when , , and
Figure 3.5 Performance of Stage 1 for various SNRs with , , , , and : (...
Figure 3.6 Performance of Stage 1 for various values of with , , , ,...
Figure 3.7 Performance of Stage 1 for various values of with , , , ,...
Figure 3.8 Performance of Stage 1 for various values of with , , , ,...
Figure 3.9 Performance of TS approach and SS approach for various values of
Figure 3.10 Performance of TS approach and SS approach for various values of...
Figure 3.11 Performance of TS and SS approach for various values of with
Chapter 4
Figure 4.1 A typical uplink massive MIMO System.
Figure 4.2 Flowchart for classifying Cases 1–3.
Figure 4.3 Feature versus with and .
Figure 4.4 Probability of correct estimation of versus with and .
Figure 4.5 Probability of resolving a two-UE collision versus with CB, v...
Figure 4.6 Success probability versus the total number of UEs in the cell ...
Figure 4.7 Probability of resolving a two-UE collision versus with CB,
Figure 4.8 Probability of resolving a two-UE collision versus with ZFB, ...
Figure 4.9 Success probability versus the total number of UEs in the cell ...
Figure 4.10 Probability of correctly estimating via SORTE versus with ZF...
Figure 4.11 Achievable probability of resolving a three-UE collision versus
Chapter 5
Figure 5.1 System model of two active devices with different RTDs.
Figure 5.2 Normalized frequencies of of the CAVI algorithm and the correla...
Figure 5.3 Performance of the CAVI algorithm to estimate RTDs for different ...
Figure 5.4 Performance of the CAVI algorithm to estimate RTDs for different ...
Figure 5.5 Performance of the CAVI algorithm and the ML approach for differe...
Figure 5.6 Performance of the CAVI algorithm and the ML approach for differe...
Figure 5.7 Performance of the CAVI algorithm and the ML approach for differe...
Figure 5.8 Conditional normalized MSE of the estimated composite CIR (withou...
Figure 5.9 Conditional probabilities of correct estimation of for given ...
Figure 5.10 Conditional probabilities of correct estimation of for given
Figure 5.11 with numbers of samples under , , , , and .
Figure 5.12 with numbers of mutations under , , , , and .
Figure 5.13 with under , , , , and .
Chapter 6
Figure 6.1 Backscatter communication system.
Figure 6.2 Ambient backscatter communications for parasite devices.
Figure 6.3 versus number of host devices, , with , , , , , and .
Figure 6.4 versus , with , , , , and .
Figure 6.5 versus number of antennas, , with , , , , and .
Figure 6.6 versus channel correlation coefficient, , with , , , , and...
Figure 6.7 versus number of host devices, , with , , , , , and .
Figure 6.8 versus , with , , , , and .
Figure 6.9 versus number of antennas, , with , , , , and .
Figure 6.10 versus channel correlation coefficient, , with , , , , ,...
Chapter 7
Figure 7.1 NOMA system with two IoT devices.
Figure 7.2 The stylized relationship of EE maximization, power minimization,...
Figure 7.3 Impact of the normalized distance between the BS and the SU on th...
Figure 7.4 Impact of the maximum constraint at the SU on the SIC’s failure...
Chapter 8
Figure 8.1 Low energy consumption, high energy efficiency, and low complexit...
Figure 8.2 Transmission power consumption and EE performance with 5 MHz band...
Figure 8.3 (a) An elementary example of CI exploitation with BPSK constellat...
Figure 8.4 The performance gain of the FJT-CI and PBF-CI designs over the co...
Figure 8.5 The symbol error rate and execution time of different precoders i...
Guide
Cover
Table of Contents
Series Page
Title Page
Copyright
Dedication
List of Figures
List of Algorithms
About the Authors
Foreword
Preface
Acknowledgements
Acronyms
Begin Reading
References
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
Moeness Amin
Jón Atli Benediktsson
Adam Drobot
James Duncan
Ekram Hossain
Brian Johnson
Hai Li
James Lyke
Joydeep Mitra
Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Diomidis Spinellis
IoT Signal Detection
Rui Han
Beihang University
Jingjing Wang
Beihang University
Lin Bai
Beihang University
Jianwei Liu
Beihang University
Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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List of Figures
Figure 1.1 Architecture of IoT
Figure 2.1 The contention-based RA
Figure 2.2 The contention-free RA
Figure 2.3 The grant-free RA
Figure 2.4 The compressed sensing
Figure 2.5 A MIMO system
Figure 2.6 The constellation of - system with ML
Figure 2.7 BER of ML detector in - MIMO system
Figure 2.8 The constellation of - system with ZF
Figure 2.9 BER of ZF detector in - MIMO system
Figure 2.10 The process of MMSE detection
Figure 2.11 The constellation of - system with MMSE
Figure 2.12 BER of MMSE detector in - system
Figure 2.13 Comparison between ZF detector and MCMC detector when
Figure 2.14 BER of MCMC detector in - MIMO system
Figure 2.15 BER of VI detection in MIMO system
Figure 2.16 The process of compressive sensing
Figure 2.17 The reconstruction residual with different observation vector sizes
Figure 3.1 Structure of NGMA systems
Figure 3.2 The tree search for the presence of (sparse) signals in a block
Figure 3.3 Complexity ratio: (a) as a function of with ; (b) as a function of with
Figure 3.4-divergence and its approximation in Eq. (3.59) when , , and : (a) as a function of with ; (b) as a function of with
Figure 3.5 Performance of Stage 1 for various SNRs with , , , , and : (a) the probabilities of FA and MD of the CAVI algorithm as functions of ; (b) -divergence as a function of
Figure 3.6 Performance of Stage 1 for various values of with , , , , and : (a) the probabilities of FA and MD of the CAVI algorithm as functions of ; (b) -divergence as a function of
Figure 3.7 Performance of Stage 1 for various values of with , , , , and : (a) the probabilities of FA and MD of the CAVI algorithm as functions of ; (b) -divergence as a function of
Figure 3.8 Performance of Stage 1 for various values of with , , , , and : (a) the probabilities of FA and MD of the CAVI algorithm as functions of ; (b) -divergence as a function of
Figure 3.9 Performance of TS approach and SS approach for various values of with , , , , and
Figure 3.10 Performance of TS approach and SS approach for various values of with , , , , and
Figure 3.11 Performance of TS and SS approach for various values of with , , , , and
Figure 4.1 A typical uplink massive MIMO System
Figure 4.2 Flowchart for classifying Cases 1–3
Figure 4.3 Feature versus with and
Figure 4.4 Probability of correct estimation of versus with and
Figure 4.5 Probability of resolving a two-UE collision versus with CB, various , , , and
Figure 4.6 Success probability versus the total number of UEs in the cell with CB, , , and
Figure 4.7 Probability of resolving a two-UE collision versus with CB, , , and
Figure 4.8 Probability of resolving a two-UE collision versus with ZFB, various , , , and
Figure 4.9 Success probability versus the total number of UEs in the cell with ZFB, , , and
Figure 4.10 Probability of correctly estimating via SORTE versus with ZFB, , , , and
Figure 4.11 Achievable probability of resolving a three-UE collision versus with and
Figure 5.1 System model of two active devices with different RTDs
Figure 5.2 Normalized frequencies of of the CAVI algorithm and the correlator-based detector when (), , , , , , and
Figure 5.3 Performance of the CAVI algorithm to estimate RTDs for different numbers of iterations when , , , , , , and
Figure 5.4 Performance of the CAVI algorithm to estimate RTDs for different values of step size , when , , , , , , and
Figure 5.5 Performance of the CAVI algorithm and the ML approach for different values of when , , , , , and
Figure 5.6 Performance of the CAVI algorithm and the ML approach for different numbers of multipaths, , when , , , , , and
Figure 5.7 Performance of the CAVI algorithm and the ML approach for different numbers of antennas, , when , , , , , and
Figure 5.8 Conditional normalized MSE of the estimated composite CIR (without CIR overlapping) when , , , and : (a) as a function of with and ; (b) as a function of with and ; (c) as a function of with and
Figure 5.9 Conditional probabilities of correct estimation of for given as functions of when , , , , and
Figure 5.10 Conditional probabilities of correct estimation of for given when , , , and : (a) as functions of the number of multipaths, , when ; (b) as functions of the length of CP, , when
Figure 5.11 with numbers of samples under , , , , and
Figure 5.12 with numbers of mutations under , , , , and
Figure 5.13 with under , , , , and
Figure 6.1 Backscatter communication system
Figure 6.2 Ambient backscatter communications for parasite devices
Figure 6.3 versus number of host devices, , with , , , , , and
Figure 6.4 versus , with , , , , and
Figure 6.5 versus number of antennas, , with , , , , and
Figure 6.6 versus channel correlation coefficient, , with , , , , and
Figure 6.7 versus number of host devices, , with , , , , , and
Figure 6.8 versus , with , , , , and
Figure 6.9 versus number of antennas, , with , , , , and
Figure 6.10 versus channel correlation coefficient, , with , , , , , and
Figure 7.1 NOMA system with two IoT devices
Figure 7.2 The stylized relationship of EE maximization, power minimization, and throughput maximization problems
Figure 7.3 Impact of the normalized distance between the BS and the SU on the value of EE and throughput, with . (a) normalized distance between the BS and the SU and (b) normalized distance between the BS and the SU
Figure 7.4 Impact of the maximum constraint at the SU on the SIC’s failure probability, where the SU is in the middle of the BS and the WU
Figure 8.1 Low energy consumption, high energy efficiency, and low complexity techniques are preferable to enable an energy-efficient multi-cluster coordinated IIoT system
Figure 8.2 Transmission power consumption and EE performance with 5 MHz bandwidth. (a) transmission power consumption performance and (b) EE performance with different coordination designs
Figure 8.3 (a) An elementary example of CI exploitation with BPSK constellation; (b) A quadrature phase shift keying (QPSK) constellation example with CI precoding exploits interference as a beneficial element; (c) A schematic representation of - constellation points
Figure 8.4 The performance gain of the FJT-CI and PBF-CI designs over the conventional coordination techniques
Figure 8.5 The symbol error rate and execution time of different precoders in the FJT scenario. (a) Symbol error rate of different precoders and (b) execution time of different precoders
List of Algorithms
Algorithm 2.1 ML Detection
Algorithm 2.2 ZF Detection
Algorithm 2.3 MMSE Detection
Algorithm 2.4 Gibbs Sampler
Algorithm 2.5 VI Detection
Algorithm 2.6 CS Detection
Algorithm 3.1 VI Detection for Sparse Signal
Algorithm 5.1 The GA-MCMC Gibbs Sampler
Algorithm 7.1 EE Oriented FD Cooperative NOMA Algorithm
About the Authors
Rui Han received the PhD degree in cyber security from Beihang University, Beijing, China, in 2022. From 2022 to 2024, Dr. Han was a research fellow at National Research Center, Tsinghua University, Beijing, China. Her current research interests include the Internet of things (IoT), unmanned aerial vehicle (UAV) communications, and satellite communications.
Jingjing Wang received his BSc degree in electronic information engineering from the Dalian University of Technology, Liaoning, China, in 2014 and the PhD degree in information and communication engineering from the Tsinghua University, Beijing, China, in 2019, both with the highest honors. From 2017 to 2018, he visited the next-generation wireless group chaired by Prof. Lajos Hanzo in the University of Southampton, UK. Dr. Wang is currently a professor at the School of Cyber Science and Technology, Beihang University, Beijing, China. His research interests include AI-enhanced next-generation wireless networks, UAV networking, and swarm intelligence. He has published over 100 IEEE Journal/Conference papers. Dr. Wang was a recipient of the Best Journal Paper Award of IEEE ComSoc Technical Committee on Green Communications & Computing in 2018, and the Best Paper Award of IEEE ICC and IWCMC in 2019. He is currently serving as an editor for the IEEE Wireless Communications Letter and the IEEE Open Journal of the Communications Society. He has served as a guest editor for IEEE Internet of Things Journal.
Lin Bai received the BSc degree in electronic and information engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2004, the MSc degree (Hons.) in communication systems from the University of Wales, Swansea, UK, in 2007, and the PhD degree in advanced telecommunications from the School of Engineering, Swansea University, UK, in 2010. Since 2011, he has been with Beihang University (Beijing University of Aeronautics and Astronautics, BUAA), Beijing, China, where he is currently a professor at the School of Cyber Science and Technology. His research interests include the security of space-air-ground integrated network (SAGIN), broadband wireless ad hoc network, unmanned aerial vehicle (UAV) communications, and Internet of Things (IoT). He has authored two books published by Springer in 2012 and 2014. He was the Symposium Co-Chair of IEEE GLOBECOM 2019, IEEE VTC 2021, and IEEE/CIC ICCC 2024, the Tutorial Co-Chair of IEEE/CIC ICCC 2019. He is the founding chair of IEEE ComSoc Wireless Communications Technical Committee Special Interest Group (SIG) on Space Air Ground Integrated (SAGI) Communications. He has served as an editor for IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters, a lead guest editor for IEEE Wireless Communications, and a guest editor for IEEE Internet of Things Journal. He is currently serving as an editor for IEEE Transactions on Wireless Communications and IEEE Transactions on Mobile Computing, and the managing editor for Journal of Communications and Information Networks. He is a distinguished lecturer of the IEEE Communications Society and the IEEE Vehicular Technology Society.
Jianwei Liu received the BSc and MSc degrees in electronic and information from Shandong University, Shandong, China, in 1985 and 1988. He received the PhD degree in communication and electronic system from Xidian University, Shaanxi, China, in 1998. Currently, he is a professor with the School of Cyber Science and Technology, Beihang University, Beijing, China. His research interests include wireless communication network, cryptography, and network security.
Foreword
Over the past decades, Internet of Things (IoT) has been well developed to become one of the most important technologies in the 21st century, which aims to provide heterogeneous services for massively connected devices. Evidently, massive connectivity in IoT causes severe access congestion, and signal collision and signal superposition occur frequently. Therefore, signal detection becomes crucial in IoT communication systems. This book provides a range of key techniques to support massive IoT devices, and various signal detection techniques are explained in the context in terms of sparse signal detection, collided signal detection, round-trip delay estimation, backscatter signal division, etc. It makes an easy-to-follow presentation from the elementary to the profound level with a carefully balanced blend of theoretical elements and applications.
My colleagues, Dr. Han, Prof. Wang, Prof. Bai, and Prof. Liu, have worked on this topic for many years. They have made good achievements and published a number of papers within this topic. This book provides fundamentals of signal detection and estimation together with new results that have been developed for IoT applications, which is ideal for both graduate students and practicing engineers in wireless communications.
AcademicianChinese Academy of EngineeringBeijing
Quan Yu
Preface
Machine-type communication (MTC) is expected to play a crucial role in supporting a number of devices for Internet of Things (IoT). Due to the fact that most IoT devices have sparse activity and low signaling overhead, random access (RA) can be employed for MTC to provide an efficient way to support massive IoT devices with minimized network overload.
However, there exist many problems in IoT RA, e.g., signal collision, signal superposition. In order to face these challenges, we focus on the signal detection for IoT in terms of sparse signal detection, collided signal detection, round-trip delay estimation, and backscatter signal division.
Our book mainly focuses on the signal detection for RA in IoT, which covers the fundamentals of signal detection with two chapters dedicated to important background materials. Besides, various signal detection and estimation techniques are explained, e.g., variational inference algorithm, compressive sensing reconstruction algorithm, and we include a number of recent research outcomes that are useful for those experts in this area. In addition, the techniques are then analyzed using performance analysis tools, and simulation results are also given to help readers to understand the theorem and algorithm.
