Compressed sensing for distributed systems

Responsibility
Giulio Coluccia, Chiara Ravazzi, Enrico Magli.
Digital
PDF
text file
Publication
Singapore : Springer, [2015]
Copyright notice
©2015
Physical description
1 online resource : color illustrations
Series
SpringerBriefs in electrical and computer engineering. Signal processing.

Online

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Description

Creators/Contributors

Author/Creator
Coluccia, Giulio, author.
Contributor
Ravazzi, Chiara, author.
Magli, Enrico, author.

Contents/Summary

Bibliography
Includes bibliographical references.
Contents
  • Introduction
  • Distributed Compressed Sensing
  • Rate?Distortion Theory of Distributed Compressed Sensing
  • Centralized Joint Recovery
  • Distributed Recovery
  • Conclusions.
  • Machine generated contents note: 1. Introduction
  • References
  • 2. Distributed Compressed Sensing
  • 2.1. Compressed Sensing for Single Sources
  • 2.1.1. Sensing Model
  • 2.1.2. Sparse Recovery
  • 2.1.3. Iterative Thresholding Algorithms
  • 2.2. Compressed Sensing for Distributed Systems
  • 2.2.1. Distributed Setup
  • 2.3. Joint Sparsity Models
  • 2.4. Reconstruction for Distributed Systems
  • References
  • 3. Rate-Distortion Theory of Distributed Compressed Sensing
  • 3.1. Introduction
  • 3.2. Source Coding with Side Information at the Decoder
  • 3.3. Rate-Distortion Functions of Single-Source Compressed Sensing
  • 3.3.1. Single-Source System Model
  • 3.3.2. Rate-Distortion Functions of Measurement Vector
  • 3.3.3. Rate-Distortion Functions of the Reconstruction
  • 3.4. Rate-Distortion Functions of Distributed Compressed Sensing
  • 3.4.1. Distributed System Model
  • 3.4.2. Rate-Distortion Functions of Measurement Vector
  • 3.4.3. Rate-Distortion Functions of the Reconstruction
  • References
  • 4. Centralized Joint Recovery
  • 4.1. Baseline Algorithms
  • 4.1.1. Recovery Strategy for JSM-1: γ-Weighted l1-Norm Minimization
  • 4.1.2. Recovery Strategies for JSM-3
  • 4.2. Texas Hold'em
  • 4.3. Algorithms Exploiting Side Information
  • 4.3.1. Intersect and Sort algorithms
  • 4.3.2. Algorithms Based on Difference of Innovations
  • 4.4. Performance Comparison
  • References
  • 5. Distributed Recovery
  • 5.1. Introduction
  • 5.2. Problem Setting
  • 5.2.1. Consensus-Based Optimization Model
  • 5.2.2. Communication and Processing Model
  • 5.2.3. Distributed Algorithms for Lasso Estimation Problem
  • 5.2.4. Energy Saving Algorithms: Distributed Sparsity Constrained Least Squares
  • 5.3. Beyond Single Source Estimation: Distributed Recovery of Correlated Signals
  • References.
Summary
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.

Bibliographic information

Publication date
2015
Copyright date
2015
Series
SpringerBriefs in electrical and computer engineering, Signal processing, 2191-8112
ISBN
9789812873903 (electronic bk.)
9812873902 (electronic bk.)
9812873899 (print)
9789812873897 (print)
9789812873897
DOI
10.1007/978-981-287-390-3

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