IEICE TRANSACTIONS on Communications
Measurement Matrices Construction for Compressed Sensing Based on Finite Field Quasi-Cyclic LDPC Codes
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E99-B No.11 pp.2332-2339
- Publication Date
- 2016/11/01
- Publicized
- 2016/06/16
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2016EBP3018
- Type of Manuscript
- PAPER
- Category
- Fundamental Theories for Communications
Authors
Hua XU
Yancheng Teachers University
Hao YANG
Yancheng Teachers University
Wenjuan SHI
Yancheng Teachers University
Keyword
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Hua XU, Hao YANG, Wenjuan SHI, "Measurement Matrices Construction for Compressed Sensing Based on Finite Field Quasi-Cyclic LDPC Codes" in IEICE TRANSACTIONS on Communications,
vol. E99-B, no. 11, pp. 2332-2339, November 2016, doi: 10.1587/transcom.2016EBP3018.
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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2016EBP3018/_p
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@ARTICLE{e99-b_11_2332,
author={Hua XU, Hao YANG, Wenjuan SHI, },
journal={IEICE TRANSACTIONS on Communications},
title={Measurement Matrices Construction for Compressed Sensing Based on Finite Field Quasi-Cyclic LDPC Codes},
year={2016},
volume={E99-B},
number={11},
pages={2332-2339},
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.},
keywords={},
doi={10.1587/transcom.2016EBP3018},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Measurement Matrices Construction for Compressed Sensing Based on Finite Field Quasi-Cyclic LDPC Codes
T2 - IEICE TRANSACTIONS on Communications
SP - 2332
EP - 2339
AU - Hua XU
AU - Hao YANG
AU - Wenjuan SHI
PY - 2016
DO - 10.1587/transcom.2016EBP3018
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E99-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2016
AB - 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.
ER -
English
