Coluccia G., Ravazzi C., Magli E. Compressed Sensing for Distributed Systems

Springer, 2015. — 104 p. — (Springer Briefs in Electrical and Computer Engineering).This book presents a survey of the state of the art 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.Distributed Compressed Sensing .
Compressed Sensing for Single Sources.
Sensing Model
Sparse Recovery
Iterative Thresholding Algorithms
Compressed Sensing for Distributed Systems
Distributed Setup
Joint Sparsity Models
Reconstruction for Distributed SystemsRate-Distortion Theory of Distributed Compressed SensingSource Coding with Side Information at the Decoder
Rate-Distortion Functions of Single-Source Compressed Sensing
Single-Source System Model
Rate-Distortion Functions of Measurement Vector.
Rate-Distortion Functions of the Reconstruction
Rate-Distortion Functions of Distributed Compressed Sensing
Distributed System Model
Rate-Distortion Functions of Measurement Vector.
Rate-Distortion Functions of the ReconstructionCentralized Joint Recovery
Baseline Algorithms
Recovery Strategy for JSM-1: γ-Weighted‘1-Norm Minimization
Recovery Strategies for JSM-3
Texas Hold’em
Algorithms Exploiting Side Information
The Intersect and Sort algorithms
Algorithms Based on Difference of Innovations
Performance ComparisonDistributed RecoveryProblem Setting
Consensus-Based Optimization Model
Communication and Processing Model
Distributed Algorithms for Lasso Estimation Problem
Energy Saving Algorithms: Distributed Sparsity Constrained Least Squares
Beyond Single Source Estimation: Distributed Recovery of Correlated SignalsConclusions