A Low-Computation Compressive Wideband Spectrum Sensing Algorithm Based on Multirate Coprime Sampling
Summary :
Compressed sensing (CS)-based wideband spectrum sensing has been a hot topic because it can cut high signal acquisition costs. However, using CS-based approaches, the spectral recovery requires large computational complexity. This letter proposes a wideband spectrum sensing algorithm based on multirate coprime sampling. It can detect the entire wideband directly from sub-Nyquist samples without spectral recovery, thus it brings a significant reduction of computational complexity. Compared with the excellent spectral recovery algorithm, i.e., orthogonal matching pursuit, our algorithm can maintain good sensing performance with computational complexity being several orders of magnitude lower.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.4 pp.1060-1065
- Publication Date
- 2017/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.1060
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Shiyu REN
Beijing University of Posts and Telecommunications
Zhimin ZENG
Beijing University of Posts and Telecommunications
Caili GUO
Beijing University of Posts and Telecommunications
Xuekang SUN
Beijing University of Posts and Telecommunications
Keyword
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Shiyu REN, Zhimin ZENG, Caili GUO, Xuekang SUN, "A Low-Computation Compressive Wideband Spectrum Sensing Algorithm Based on Multirate Coprime Sampling" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 4, pp. 1060-1065, April 2017, doi: 10.1587/transfun.E100.A.1060.
Abstract: Compressed sensing (CS)-based wideband spectrum sensing has been a hot topic because it can cut high signal acquisition costs. However, using CS-based approaches, the spectral recovery requires large computational complexity. This letter proposes a wideband spectrum sensing algorithm based on multirate coprime sampling. It can detect the entire wideband directly from sub-Nyquist samples without spectral recovery, thus it brings a significant reduction of computational complexity. Compared with the excellent spectral recovery algorithm, i.e., orthogonal matching pursuit, our algorithm can maintain good sensing performance with computational complexity being several orders of magnitude lower.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.1060/_p
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@ARTICLE{e100-a_4_1060,
author={Shiyu REN, Zhimin ZENG, Caili GUO, Xuekang SUN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Low-Computation Compressive Wideband Spectrum Sensing Algorithm Based on Multirate Coprime Sampling},
year={2017},
volume={E100-A},
number={4},
pages={1060-1065},
abstract={Compressed sensing (CS)-based wideband spectrum sensing has been a hot topic because it can cut high signal acquisition costs. However, using CS-based approaches, the spectral recovery requires large computational complexity. This letter proposes a wideband spectrum sensing algorithm based on multirate coprime sampling. It can detect the entire wideband directly from sub-Nyquist samples without spectral recovery, thus it brings a significant reduction of computational complexity. Compared with the excellent spectral recovery algorithm, i.e., orthogonal matching pursuit, our algorithm can maintain good sensing performance with computational complexity being several orders of magnitude lower.},
keywords={},
doi={10.1587/transfun.E100.A.1060},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - A Low-Computation Compressive Wideband Spectrum Sensing Algorithm Based on Multirate Coprime Sampling
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1060
EP - 1065
AU - Shiyu REN
AU - Zhimin ZENG
AU - Caili GUO
AU - Xuekang SUN
PY - 2017
DO - 10.1587/transfun.E100.A.1060
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2017
AB - Compressed sensing (CS)-based wideband spectrum sensing has been a hot topic because it can cut high signal acquisition costs. However, using CS-based approaches, the spectral recovery requires large computational complexity. This letter proposes a wideband spectrum sensing algorithm based on multirate coprime sampling. It can detect the entire wideband directly from sub-Nyquist samples without spectral recovery, thus it brings a significant reduction of computational complexity. Compared with the excellent spectral recovery algorithm, i.e., orthogonal matching pursuit, our algorithm can maintain good sensing performance with computational complexity being several orders of magnitude lower.
ER -
English
