• Density Evolution Analysis of...

Treffer: Density Evolution Analysis of Node-Based Verification-Based Algorithms in Compressed Sensing

Title:
Density Evolution Analysis of Node-Based Verification-Based Algorithms in Compressed Sensing
Authors:
EFTEKHARI, Yaser, HEIDARZADEH, Anoosheh, BANIHASHEMI, Amir H, LAMBADARIS, Loannis
Source:
IEEE transactions on information theory. 58(10):6616-6645
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2012.
Publication Year:
2012
Physical Description:
print, 45 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie de l'information, Information theory, Théorie du signal et des communications, Signal and communications theory, Codage, codes, Coding, codes, Echantillonnage, quantification, Sampling, quantization, Code correcteur erreur, Error correcting code, Código corrector error, Algorithme, Algorithm, Algoritmo, Approche déterministe, Deterministic approach, Enfoque determinista, Codage canal, Channel coding, Code contrôle parité, Parity check codes, Décodage itératif, Iterative decoding, Effet concentration, Concentration effect, Efecto concentración, Envoi message, Message passing, Equation différentielle, Differential equation, Ecuación diferencial, Evaluation performance, Performance evaluation, Evaluación prestación, Implémentation, Implementation, Implementación, Matrice creuse, Sparse matrix, Matriz dispersa, Méthode itérative, Iterative method, Método iterativo, Poursuite cible, Target tracking, Signal entrée, Input signal, Señal entrada, Simulation, Simulación, Système grande taille, Large scale system, Sistema gran escala, Echantillonnage parcimonieux, Compressed sensing, -Asymptotic analysis, channel coding, compressed sensing, density evolution, iterative decoding algorithms, iterative recovery algorithms, low-complexity compressed sensing, low-density parity-check (LDPC) codes, message-passing algorithms, sparse graphs, sparse sensing matrix, success threshold, verification-based recovery algorithms
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
ISSN:
0018-9448
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26403685
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.26403685
Database:
PASCAL Archive

Weitere Informationen

In this paper, we present a new approach for the analysis of iterative node-based verification-based (NB-VB) recovery algorithms in the context of compressed sensing. These algorithms are particularly interesting due to their low complexity (linear in the signal dimension n). The asymptotic analysis predicts the fraction of unverified signal elements at each iteration ℓ in the asymptotic regime where n → ∞. The analysis is similar in nature to the well-known density evolution technique commonly used to analyze iterative decoding algorithms. To perform the analysis, a message-passing interpretation of NB-VB algorithms is provided. This interpretation lacks the extrinsic nature of standard message-passing algorithms to which density evolution is usually applied. This requires a number of nontrivial modifications in the analysis. The analysis tracks the average performance of the recovery algorithms over the ensembles of input signals and sensing matrices as a function of ℓ. Concentration results are devised to demonstrate that the performance of the recovery algorithms applied to any choice of the input signal over any realization of the sensing matrix follows the deterministic results of the analysis closely. Simulation results are also provided which demonstrate that the proposed asymptotic analysis matches the performance of recovery algorithms for large but finite values of n. Compared to the existing technique for the analysis of NB-VB algorithms, which is based on numerically solving a large system of coupled differential equations, the proposed method is more accurate and simpler to implement.