Low-overhead Communications in IoT Networks (eBook)

Structured Signal Processing Approaches

Yuanming Shi, Jialin Dong, Jun Zhang (Autoren)

eBook Download: PDF

2020 | 1st ed. 2020
XIV, 152 Seiten
Springer Singapore (Verlag)
978-981-15-3870-4 (ISBN)

Infos

The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains.

This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.

Yuanming Shi received his B.S. degree in Electronic Engineering from Tsinghua University, Beijing, China, in 2011, and his Ph.D. in Electronic and Computer Engineering from The Hong Kong University of Science and Technology (HKUST), in 2015. Since September 2015, he has been at the School of Information Science and Technology at ShanghaiTech University, where he is currently a tenured Associate Professor. He visited the University of California, Berkeley, USA, from October 2016 to February 2017. Dr. Shi is a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, and the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society. His research areas include optimization, statistics, machine learning and signal processing, and their applications to 6G, IoT and AI.

Jialin Dong received her B.S. degree in Communication Engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2017. She is currently a graduate student at ShanghaiTech University's School of Information Science and Technology, and is also a Research Assistant at the Department of Electronic and Information Engineering at the Hong Kong Polytechnic University. Her research interests include mathematical optimization and high-dimensional probability.

Jun Zhang received his Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin in 2009. He is an Assistant Professor at the Department of Electronic and Information Engineering at the Hong Kong Polytechnic University (PolyU). His research interests include wireless communications and networking, mobile edge computing and edge learning, distributed learning and optimization, and big data analytics. Dr. Zhang co-authored the books 'Fundamentals of LTE' (Prentice-Hall, 2010), and 'Stochastic Geometry Analysis of Multi-Antenna Wireless Networks' (Springer, 2019). He is a co-recipient of the 2019 IEEE Communications Society & Information Theory Society Joint Paper Award, the 2016 Marconi Prize Paper Award in Wireless Communications, and the 2014 Best Paper Award for the EURASIP Journal on Advances in Signal Processing. Two papers he co-authored received the IEEE Signal Processing Society's Young Author Best Paper Award in 2016 and 2018, respectively. He also received the 2016 IEEE ComSoc Asia-Pacific Best Young Researcher Award. He is an editor of IEEE Transactions on Wireless Communications, IEEE Transactions on Communications, and the Journal of Communications and Information Networks.

Inhalt

Preface 5
Acknowledgements 8
Contents 9
Mathematical Notations 12
1 Introduction 14
1.1 Low-Overhead Communications in IoT Networks 14
1.1.1 Grant-Free Random Access 15
1.1.2 Pilot-Free Communications 17
1.1.3 Identification-Free Communications 18
1.2 Structured Signal Processing 18
1.2.1 Example: Compressed Sensing 19
1.2.2 General Structured Signal Processing 20
1.3 Outline 21
References 23
2 Sparse Linear Model 25
2.1 Joint Activity Detection and Channel Estimation 25
2.2 Problem Formulation 26
2.2.1 Single-Antenna Scenario 27
2.2.2 Multiple-Antenna Scenario 28
2.3 Convex Relaxation Approach 29
2.3.1 Method: p-Norm Minimization 29
2.3.2 Algorithm: Smoothed Primal-Dual First-Order Methods 30
2.3.3 Analysis: Conic Integral Geometry 33
2.3.3.1 Conic Integral Geometry for the Sparse Linear Model 33
2.3.3.2 Computation and Estimation Trade-Offs 36
2.3.3.3 Simulation Results 37
2.4 Iterative Thresholding Algorithm 39
2.4.1 Algorithm: Approximate Message Passing 40
2.4.2 Analysis: State Evolution 41
2.4.2.1 State Evolution 41
2.4.2.2 Denoiser Designs 41
2.4.2.3 Asymptotic Performance of Device Activity Detection 42
2.4.2.4 Simulation Results 43
2.5 Summary 44
References 44
3 Blind Demixing 47
3.1 Joint Data Decoding and Channel Estimation 47
3.2 Problem Formulation 49
3.2.1 Cyclic Convolution 49
3.2.2 System Model 50
3.2.3 Representation in the Fourier Domain 50
3.3 Convex Relaxation Approach 52
3.3.1 Method: Nuclear Norm Minimization 52
3.3.2 Theoretical Analysis 53
3.4 Nonconvex Approaches 54
3.4.1 Regularized Wirtinger Flow 55
3.4.2 Regularization-Free Wirtinger Flow 57
3.4.3 Riemannian Optimization Algorithm 61
3.4.3.1 An Example on Riemannian Optimization 61
3.4.3.2 Riemannian Optimization on Product Manifolds for Blind Demixing 62
3.4.4 Simulation Results 67
3.5 Summary 70
References 70
4 Sparse Blind Demixing 71
4.1 Joint Device Activity Detection, Data Decoding, and Channel Estimation 71
4.2 Problem Formulation 72
4.2.1 Single-Antenna Scenario 72
4.2.2 Multiple-Antenna Scenario 73
4.3 Convex Relaxation Approach 73
4.4 Difference-of-Convex-Functions (DC) Programming Approach 75
4.4.1 Sparse and Low-Rank Optimization 76
4.4.2 A DC Formulation for Rank Constraint 77
4.4.3 DC Algorithm for Minimizing a DC Objective 78
4.4.4 Simulations 79
4.5 Smoothed Riemannian Optimization on Product Manifolds 81
4.5.1 Optimization on Product Manifolds 81
4.5.2 Smoothed Riemannian Optimization 82
4.5.3 Simulation Results 83
4.6 Summary 85
References 85
5 Shuffled Linear Regression 87
5.1 Joint Data Decoding and Device Identification 87
5.2 Problem Formulation 89
5.3 Maximum Likelihood Estimation Based Approaches 90
5.3.1 Sorting Based Algorithms 90
5.3.2 Approximation Algorithm 92
5.4 Algebraic-Geometric Approach 94
5.4.1 Eliminating ? via Symmetric Polynomials 95
5.4.2 Theoretical Analysis 97
5.4.2.1 Exact Data 97
5.4.2.2 Corrupted Data 98
5.4.3 Algebraically Initialized Expectation-Maximization 98
5.4.4 Simulation Results 100
5.5 Summary 101
References 101
6 Learning Augmented Methods 103
6.1 Structured Signal Processing Under a Generative Prior 103
6.2 Joint Design of Measurement Matrix and Sparse Support Recovery 106
6.3 Deep-Learning-Based AMP 108
6.3.1 Learned AMP 109
6.3.2 Learned Vector-AMP 111
6.3.3 Learned ISTA for Group Row Sparsity 112
6.3.3.1 Simulations Results 114
6.4 Summary 115
References 117
7 Conclusions and Discussions 119
7.1 Summary 119
7.2 Discussions 121
References 121
8 Appendix 123
8.1 Conic Integral Geometry 123
8.1.1 The Kinematic Formula for Convex Cones 123
8.1.2 Intrinsic Volumes and the Statistical Dimension 124
8.1.3 The Approximate Kinematic Formula 126
8.1.4 Computing the Statistical Dimension 126
8.2 Proof of Proposition 2.1 127
8.3 Proof of Theorem 3.3 128
8.3.1 Proof of Lemma 8.4 141
8.4 Theoretical Analysis of Wirtinger Flow with Random Initialization for Blind Demixing 151
8.5 The Basic Concepts on Riemannian Optimization 154
8.6 Proof of Theorem 3.4 159
8.7 Basic Concepts in Algebraic–Geometric Theory 161
8.7.1 Geometric Characterization of Dimension 161
References 163

Produkt-Details

• Erscheint lt. Verlag: 17.4.2020
• Zusatzinfo: XIV, 152 p. 350 illus., 19 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Informatik ► Weitere Themen ► Hardware
• Themenwelt: Technik
• Schlagworte: Convex Optimization • digital signal processing • internet of things • Nonconvex Optimization • wireless communications
• ISBN-10: 981-15-3870-0 / 9811538700
• ISBN-13: 978-981-15-3870-4 / 9789811538704