Paper Info
Title
Measurement Matrices Construction For Compressed Sensing Based On Finite Field Quasi-Cyclic Ldpc Codes
Abstract
Measurement matrix construction is critically important to signal sampling and reconstruction for compressed sensing. From a practical point of view, deterministic construction of the measurement matrix is better than random construction. In this paper, we propose a novel deterministic method to construct a measurement matrix for compressed sensing, CS-FF (compressed sensing-finite field) algorithm. For this proposed algorithm, the constructed measurement matrix is from the finite field Quasi-cyclic Low Density Parity Check (QC-LDPC) code and thus it has quasi-cyclic structure. Furthermore, we construct three groups of measurement matrices. The first group matrices are the proposed matrix and other matrices including deterministic construction matrices and random construction matrices. The other two group matrices are both constructed by our method. We compare the recovery performance of these matrices. Simulation results demonstrate that the recovery performance of our matrix is superior to that of the other matrices. In addition, simulation results show that the compression ratio is an important parameter to analyse and predict the recovery performance of the proposed measurement matrix. Moreover, these matrices have less storage requirement than that of a random one, and they achieve a better trade-off between complexity and performance. Therefore, from practical perspective, the proposed scheme is hardware friendly and easily implemented, and it is suitable to compressed sensing for its quasi-cyclic structure and good recovery performance.
Year
DOI
Venue
2016
10.1587/transcom.2016EBP3018
IEICE TRANSACTIONS ON COMMUNICATIONS
Keywords
Field
DocType
compressed sensing, mutual coherence, finite field QC-LDPC codes, compression ratio
Topology,Finite field,Computer science,Matrix (mathematics),Low-density parity-check code,Theoretical computer science,Compression ratio,Mutual coherence,Compressed sensing,Imagination,Distributed computing
Journal
Volume
Issue
ISSN
E99B
11
0916-8516
Citations 
PageRank 
References 
0
0.34
21
Authors
3
Name
Order
Citations
PageRank
Hua Xu153.57
Hao, Y.27417.57
Wenjuan Shi311.37