IEICE TRANSACTIONS on Communications
Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing
Summary :
This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E103-B No.12 pp.1462-1469
- Publication Date
- 2020/12/01
- Publicized
- 2020/06/22
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2019EBP3175
- Type of Manuscript
- PAPER
- Category
- Terrestrial Wireless Communication/Broadcasting Technologies
Authors
Shusuke NARIEDA
Mie Univ.
Daiki CHO
Tokyo Univ. of Agric. and Technol.
Hiromichi OGASAWARA
Akashi Coll.
Kenta UMEBAYASHI
Tokyo Univ. of Agric. and Technol.
Takeo FUJII
The Univ. Electro-Communication
Hiroshi NARUSE
Mie Univ.
Keyword
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Shusuke NARIEDA, Daiki CHO, Hiromichi OGASAWARA, Kenta UMEBAYASHI, Takeo FUJII, Hiroshi NARUSE, "Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing" in IEICE TRANSACTIONS on Communications,
vol. E103-B, no. 12, pp. 1462-1469, December 2020, doi: 10.1587/transcom.2019EBP3175.
Abstract: This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2019EBP3175/_p
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@ARTICLE{e103-b_12_1462,
author={Shusuke NARIEDA, Daiki CHO, Hiromichi OGASAWARA, Kenta UMEBAYASHI, Takeo FUJII, Hiroshi NARUSE, },
journal={IEICE TRANSACTIONS on Communications},
title={Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing},
year={2020},
volume={E103-B},
number={12},
pages={1462-1469},
abstract={This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.},
keywords={},
doi={10.1587/transcom.2019EBP3175},
ISSN={1745-1345},
month={December},}
Copy
TY - JOUR
TI - Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing
T2 - IEICE TRANSACTIONS on Communications
SP - 1462
EP - 1469
AU - Shusuke NARIEDA
AU - Daiki CHO
AU - Hiromichi OGASAWARA
AU - Kenta UMEBAYASHI
AU - Takeo FUJII
AU - Hiroshi NARUSE
PY - 2020
DO - 10.1587/transcom.2019EBP3175
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E103-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2020
AB - This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.
ER -
English
