A Low Computational Complexity Algorithm for Compressive Wideband Spectrum Sensing
Summary :
Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.1 pp.294-300
- Publication Date
- 2017/01/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.294
- 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
Kun SU
Beijing University of Posts and Telecommunications
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Shiyu REN, Zhimin ZENG, Caili GUO, Xuekang SUN, Kun SU, "A Low Computational Complexity Algorithm for Compressive Wideband Spectrum Sensing" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 1, pp. 294-300, January 2017, doi: 10.1587/transfun.E100.A.294.
Abstract: Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.294/_p
Copy
@ARTICLE{e100-a_1_294,
author={Shiyu REN, Zhimin ZENG, Caili GUO, Xuekang SUN, Kun SU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Low Computational Complexity Algorithm for Compressive Wideband Spectrum Sensing},
year={2017},
volume={E100-A},
number={1},
pages={294-300},
abstract={Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.},
keywords={},
doi={10.1587/transfun.E100.A.294},
ISSN={1745-1337},
month={January},}
Copy
TY - JOUR
TI - A Low Computational Complexity Algorithm for Compressive Wideband Spectrum Sensing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 294
EP - 300
AU - Shiyu REN
AU - Zhimin ZENG
AU - Caili GUO
AU - Xuekang SUN
AU - Kun SU
PY - 2017
DO - 10.1587/transfun.E100.A.294
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2017
AB - Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.
ER -
English
