A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence

Haiqiang LIU; Gang HUA; Hongsheng YIN; Aichun ZHU; Ran CUI

doi:10.1587/transinf.2018EDP7324

A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence

Haiqiang LIU, Gang HUA, Hongsheng YIN, Aichun ZHU, Ran CUI

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Compressed sensing is an effective compression algorithm. It is widely used to measure signals in distributed sensor networks (DSNs). Considering the limited resources of DSNs, the measurement matrices used in DSNs must be simple. In this paper, we construct a deterministic measurement matrix based on Gordon-Mills-Welch (GMW) sequence. The column vectors of the proposed measurement matrix are generated by cyclically shifting a GMW sequence. Compared with some state-of-the-art measurement matrices, the proposed measurement matrix has relative lower computational complexity and needs less storage space. It is suitable for resource-constrained DSNs. Moreover, because the proposed measurement matrix can be realized by using simple shift register, it is more practical. The simulation result shows that, in terms of recovery quality, the proposed measurement matrix performs better than some state-of-the-art measurement matrices.

Publication: IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1296-1301

Publication Date: 2019/07/01

Publicized: 2019/04/16

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2018EDP7324

Type of Manuscript: PAPER

Category: Information Network

Authors

Haiqiang LIU
  China University of Mining and Technology
Gang HUA
  China University of Mining and Technology
Hongsheng YIN
  China University of Mining and Technology
Aichun ZHU
  Nanjing Tech University
Ran CUI
  China University of Mining and Technology

Keyword

distributed sensor networks, compressed sensing, Gordon-Mills-Welch sequence, pseudorandom sequence

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Haiqiang LIU, Gang HUA, Hongsheng YIN, Aichun ZHU, Ran CUI, "A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence" in IEICE TRANSACTIONS on Information, vol. E102-D, no. 7, pp. 1296-1301, July 2019, doi: 10.1587/transinf.2018EDP7324.
Abstract: Compressed sensing is an effective compression algorithm. It is widely used to measure signals in distributed sensor networks (DSNs). Considering the limited resources of DSNs, the measurement matrices used in DSNs must be simple. In this paper, we construct a deterministic measurement matrix based on Gordon-Mills-Welch (GMW) sequence. The column vectors of the proposed measurement matrix are generated by cyclically shifting a GMW sequence. Compared with some state-of-the-art measurement matrices, the proposed measurement matrix has relative lower computational complexity and needs less storage space. It is suitable for resource-constrained DSNs. Moreover, because the proposed measurement matrix can be realized by using simple shift register, it is more practical. The simulation result shows that, in terms of recovery quality, the proposed measurement matrix performs better than some state-of-the-art measurement matrices.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7324/_p

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@ARTICLE{e102-d_7_1296,
author={Haiqiang LIU, Gang HUA, Hongsheng YIN, Aichun ZHU, Ran CUI, },
journal={IEICE TRANSACTIONS on Information},
title={A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence},
year={2019},
volume={E102-D},
number={7},
pages={1296-1301},
abstract={Compressed sensing is an effective compression algorithm. It is widely used to measure signals in distributed sensor networks (DSNs). Considering the limited resources of DSNs, the measurement matrices used in DSNs must be simple. In this paper, we construct a deterministic measurement matrix based on Gordon-Mills-Welch (GMW) sequence. The column vectors of the proposed measurement matrix are generated by cyclically shifting a GMW sequence. Compared with some state-of-the-art measurement matrices, the proposed measurement matrix has relative lower computational complexity and needs less storage space. It is suitable for resource-constrained DSNs. Moreover, because the proposed measurement matrix can be realized by using simple shift register, it is more practical. The simulation result shows that, in terms of recovery quality, the proposed measurement matrix performs better than some state-of-the-art measurement matrices.},
keywords={},
doi={10.1587/transinf.2018EDP7324},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence
T2 - IEICE TRANSACTIONS on Information
SP - 1296
EP - 1301
AU - Haiqiang LIU
AU - Gang HUA
AU - Hongsheng YIN
AU - Aichun ZHU
AU - Ran CUI
PY - 2019
DO - 10.1587/transinf.2018EDP7324
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - Compressed sensing is an effective compression algorithm. It is widely used to measure signals in distributed sensor networks (DSNs). Considering the limited resources of DSNs, the measurement matrices used in DSNs must be simple. In this paper, we construct a deterministic measurement matrix based on Gordon-Mills-Welch (GMW) sequence. The column vectors of the proposed measurement matrix are generated by cyclically shifting a GMW sequence. Compared with some state-of-the-art measurement matrices, the proposed measurement matrix has relative lower computational complexity and needs less storage space. It is suitable for resource-constrained DSNs. Moreover, because the proposed measurement matrix can be realized by using simple shift register, it is more practical. The simulation result shows that, in terms of recovery quality, the proposed measurement matrix performs better than some state-of-the-art measurement matrices.
ER -