IEICE TRANSACTIONS on Fundamentals
Sparse-Graph Codes and Peeling Decoder for Compressed Sensing
Summary :
In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.9 pp.1712-1716
- Publication Date
- 2016/09/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.1712
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Weijun ZENG
PLA University of Science and Technology
Huali WANG
PLA University of Science and Technology
Xiaofu WU
Nanjing University of Posts and Telecommunications
Hui TIAN
PLA University of Science and Technology
Keyword
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Weijun ZENG, Huali WANG, Xiaofu WU, Hui TIAN, "Sparse-Graph Codes and Peeling Decoder for Compressed Sensing" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 9, pp. 1712-1716, September 2016, doi: 10.1587/transfun.E99.A.1712.
Abstract: In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.1712/_p
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@ARTICLE{e99-a_9_1712,
author={Weijun ZENG, Huali WANG, Xiaofu WU, Hui TIAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Sparse-Graph Codes and Peeling Decoder for Compressed Sensing},
year={2016},
volume={E99-A},
number={9},
pages={1712-1716},
abstract={In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.},
keywords={},
doi={10.1587/transfun.E99.A.1712},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Sparse-Graph Codes and Peeling Decoder for Compressed Sensing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1712
EP - 1716
AU - Weijun ZENG
AU - Huali WANG
AU - Xiaofu WU
AU - Hui TIAN
PY - 2016
DO - 10.1587/transfun.E99.A.1712
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2016
AB - In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.
ER -
English
