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E-raamat: Compressed Sensing for Distributed Systems

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This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new. Remarkably, such applications are ideally suited to exploit all the benefits that compressed sensing can provide. The objective of this book is to provide the reader with a comprehensive survey of this topic, from the basic concepts to different classes of centralized and distributed reconstruction algorithms, as well as a comparison of these techniques. This book collects different contributions on these aspects. It presents the underlying theory in a complete and unified way for the first time, presenting various signal models and their use cases. It contains a theoretical part collecting latest results in rate-distortion analysis of distributed compressed sensing, as well as practical implementations of algorithms obtaining performance close to the theoretical bounds. It presents and discusses various distributed reconstruction algorithms, summarizing the theoretical reconstruction guarantees and providing a comparative analysis of their performance and complexity. In summary, this book will allow the reader to get started in the field of distributed compressed sensing from theory to practice. We believe that this book can find a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in mathematical optimization, network systems, graph theoretical methods, linear systems, stochastic systems, and randomized algorithms. To help the reader become familiar with the theory and algorithms presented, accompanying software is made available on the authors’ web site, implementing several of the algorithms described in the book. The only background required of the reader is a good knowledge of advanced calculus and linear algebra.

Arvustused

This book focuses on recent advances in the research involving compressed sensing for distributed systems. The book provides an excellent overview of the state-of-the-art solutions addressing various situations such as collaboration of nodes versus independence of nodes or including energy conservation as a goal in the search for the solution. well-written contribution to the field of distributed systems and computational sensing and will find its audience among professionals (researchers, faculty, and graduate students) working in these fields. (Stefan Robila, Computing Reviews, March, 2016)

1 Introduction
1(4)
References
4(1)
2 Distributed Compressed Sensing
5(12)
2.1 Compressed Sensing for Single Sources
5(5)
2.1.1 Sensing Model
6(1)
2.1.2 Sparse Recovery
7(2)
2.1.3 Iterative Thresholding Algorithms
9(1)
2.2 Compressed Sensing for Distributed Systems
10(2)
2.2.1 Distributed Setup
11(1)
2.3 Joint Sparsity Models
12(2)
2.4 Reconstruction for Distributed Systems
14(3)
References
15(2)
3 Rate-Distortion Theory of Distributed Compressed Sensing
17(22)
3.1 Introduction
17(2)
3.2 Source Coding with Side Information at the Decoder
19(1)
3.3 Rate-Distortion Functions of Single-Source Compressed Sensing
20(5)
3.3.1 Single-Source System Model
20(1)
3.3.2 Rate-Distortion Functions of Measurement Vector
21(3)
3.3.3 Rate-Distortion Functions of the Reconstruction
24(1)
3.4 Rate-Distortion Functions of Distributed Compressed Sensing
25(14)
3.4.1 Distributed System Model
27(1)
3.4.2 Rate-Distortion Functions of Measurement Vector
28(4)
3.4.3 Rate-Distortion Functions of the Reconstruction
32(4)
References
36(3)
4 Centralized Joint Recovery
39(16)
4.1 Baseline Algorithms
39(4)
4.1.1 Recovery Strategy for JSM-1: γ-Weighted l1-Norm Minimization
39(2)
4.1.2 Recovery Strategies for JSM-3
41(2)
4.2 Texas Hold'em
43(1)
4.3 Algorithms Exploiting Side Information
44(6)
4.3.1 The Intersect and Sort algorithms
45(2)
4.3.2 Algorithms Based on Difference of Innovations
47(3)
4.4 Performance Comparison
50(5)
References
53(2)
5 Distributed Recovery
55(42)
5.1 Introduction
55(3)
5.2 Problem Setting
58(34)
5.2.1 Consensus-Based Optimization Model
59(7)
5.2.2 Communication and Processing Model
66(1)
5.2.3 Distributed Algorithms for Lasso Estimation Problem
67(12)
5.2.4 Energy Saving Algorithms: Distributed Sparsity Constrained Least Squares
79(13)
5.3 Beyond Single Source Estimation: Distributed Recovery of Correlated Signals
92(5)
References
94(3)
6 Conclusions
97